Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy. Industrialized and developing nations are experiencing increased cancer incidence and prevalence3. Breast cancer incidence and death rates are serious public health concerns4. The World Health Organization (WHO) estimates that in 2023 there will be more than 2.3 million new instances of breast cancer globally and 685,000 deaths from the disease5. Early detection and accurate diagnosis of BRCA are crucial for effective treatment and personalized therapy. Morphological characteristics play an important role in detecting and diagnosing breast cancer. When a sample of breast tissue is obtained through a biopsy or surgical procedure, a pathologist examines the tissue under a microscope and looks for specific morphological features that are associated with breast cancer, such as abnormal cell growth, changes in cell shape or size, and the presence of cancerous cells. These morphological characteristics can provide important information about the type, stage, and aggressiveness of the cancer, which can help guide treatment decisions and predict patient outcomes. While morphological examination remains a crucial tool in the detection and diagnosis of breast cancer, it has some limitations9.Breast cancer (BRCA) is the most prevalent cancer in women, and it is characterized by the uncontrolled division and expansion of breast cells10. Advancements in microarray technology and the more recent Next Generation Sequencing (NGS) has made gene expression profiling of patients widely available, resulting in the collection of gene expression datasets corresponding to various diseases. This shift has marked a significant transformation in personalized medicine, departing from traditional descriptive "morphological" classification approaches towards a more comprehensive strategy that considers clinical characteristics and immunohistochemical biomarkers. Today, gene expression profiling has become well-integrated into routine clinical practice12. Breast cancer researchers have examined gene expression profiling in-depth, and clinical oncologists are starting to use the findings of these studies in their daily practices. Also, the early detection and treatment of different cancer types have benefited from mining gene expression level data13. Many methods are designed to accurately predict breast cancer based on gene expression data16. Computational techniques are becoming increasingly crucial in detecting breast cancer due to the rapid growth of computer technology. However, the use of computational techniques is affected by gene expression dataset characteristics such as small dataset sizes, excessive dimensionality, and unbalanced data17. Several machine learning, deep learning, and metaheuristic techniques have been created and applied to detect and classify cancer using gene expression data.The limitations of morphological characteristics in detecting and diagnosing breast cancer can lead to bias and difficulty in identification by physicians18 presented an extensive overview of recent cancer research works that utilize gene expression data from various types of cancer, including kidney, breast, ovarian, lung, liver, gallbladder and central nervous system. The review encompasses several facets of machine learning in cancer research, including cancer classification, cancer prediction, identification of biomarker genes, and using microarray and RNA-Seq data. Yuan et al.19 applied different methods of machine learning for the detection of lung cancer through the use of gene expression data. A novel computational method for detecting breast cancer was proposed by Wang et al.20 based on incorporating random forest (RF), Monte Carlo feature selection (MCFS), rough set-based rule learning, SVM, and dagging. A deep learning method that uses Stacked Denoising Autoencoder (SDAE) to identify genes that can effectively differentiate between tumor and healthy cases of breast cancer was proposed by Danaee et al.21. BRCA gene expression data from TCGA and gene expression omnibus (GEO) was analyzed by Jia et al.22. They used differentially expressed genes (DEG) and weighted gene co-expression network analysis (WGCNA) to select the most significant genes. A deep learning model combined with an artificial intelligence-based feature selection method (AIFSDL-PCD) using gene expression data was proposed by Alshareef et al.23 for detecting prostate cancer.Khalsan et al.26. Moreover, selecting the most suitable learning rates and regularization parameters can affect the model's ability to generalize to unseen data. Therefore, this paper aims to resolve these issues by finding a precise prediction model and advancing the state-of-the-art use of CNN to classify gene expression data using metaheuristic methods to optimize the CNN model.The field of cancer prediction using machine and deep learning methods based on gene expression data has seen significant progress in recent years. However, despite the progress in predicting cancer using machine and deep learning methods based on gene expression data, the existing models have some issues affecting their performance. These issues include choosing the feature representation, optimal architecture, including the number of layers and nodes, suitable model parameters, and picking the best values for weights and bias are critical steps in improving performance29. Metaheuristic optimization algorithms have been identified as an effective tool for solving large-scale optimization problems in bioinformatics. Many of these problems can be classified as NP-hard; thus, researchers have relied heavily on metaheuristic methods to address them. The metaheuristic methods allow for the efficient solution of large-scale samples while minimizing the use of computational resources. Despite the availability of various optimization methods, metaheuristic optimization algorithms are instrumental in solving optimization problems due to their flexibility in providing high-quality optimization solutions in a relatively short amount of computing time30. The use of metaheuristics models assists in solving the problems of high dimensionality, the complexity of variable relationships and noisy data peculiar to gene expression data. In addition, metaheuristics models can handle noisy and non-linear data by incorporating techniques such as randomization and simulated annealing to escape from local optima31. Chakraborty et al.32 presented a metaheuristic method for skin disease classification based on an artificial neural network. In MotieGhader et al.33, metaheuristic methods, including GA, WCC, PSO, CUK, ICA, LA, HTS, ACO, FOA, DSOS, and LCA, with an SVM classifier were used for the detection of breast cancer based on mRNA and micro-RNA expression data.Metaheuristic algorithms are optimization algorithms that search for solutions by exploring a large search space and iteratively improving candidate solutions. They have the ability to handle NP-hard problems, which are computationally intractable problems that cannot be solved using exact methods, by providing near-optimal solutions within a reasonable amount of time34. EOSA is a new optimization algorithm with excellent performance track records in different application domains39. It is population-based and bio-inspired, developed by taking clues from the Ebola virus's effective propagation. The algorithm's framework was designed based on the spread of Ebola disease (EVD)40. This research makes significant contributions by introducing a bio-inspired CNN model for detecting breast cancer using gene expression data from the TCGA repository. The AAIC method is used for pre-processing to remove the outliers' samples, thereafter, normalization and filtration were used. Furthermore, we converted the pre-processed data into 2D images that can be utilized in the CNN architecture. The study also proposes a hybrid of the proposed CNN architecture that employs the EOSA to enhance the classification performance. The proposed model showed its ability to classify the tumor and normal samples with high accuracy and reliability. In our proposed model, the best combination of weights required for the feature extraction is obtained using the EOSA algorithm to handle the classification problem. Therefore, this study presents a hybrid model that combines the proposed CNN and EOSA for the process of classification based on BRCA gene expression data. Consequently, in this study, the main contributions are as follows:Applying various pre-processing techniques  to prepare the gene expression data.Transforming the gene expression data into two-dimensional images.Proposal of a novel bio-inspired CNN architecture for the detection of breast cancer.Introducing a hybrid model that combines the proposed CNN and EOSA for the classification process.Assessing and comparing the proposed model with other metaheuristic algorithms combined with the proposed CNN.This paper proposes using the metaheuristic model EOSA-CNN for breast cancer detection using gene expression dataThe rest of the paper is structured as follows: a detailed account of the related work is given in Section “Related work”, while Section “Model Methodology” describes the model technology discussing the CNN Architecture and the Ebola Optimization Algorithm CNN Model (EOSA-CNN) along with the associated algorithms. Section “Experimentation, results and discussion” presents the experimental results with a discussion of the results. Comparison with results from the literature, the strengths and limitations of the model are also enumerated. Finally, the conclusion and the recommendations for future work are presented in Section “Conclusion and future work”.19 applied different machine-learning methods for detecting lung cancer through gene expression data. The Monte Carlo and incremental feature selection methods were used to identify the most important genes. Then, SVM and random forest (RF) were implemented, and their performances were compared. The results indicated that SVM achieved an accuracy, sensitivity, specificity, precision, and F1-measure of 100%, 93.2%, 96.7%, 93.9%, and 96.9%, respectively. These results are higher than those obtained using RF. Wang et al.20 proposed a novel computational method called Patient-derived tumor xenograft (PDX) for breast cancer detection by incorporating Monte Carlo feature selection, RF, rough set-based rule learning, SVM, and dagging. In the work of Danaee et al.21 proposed, a deep learning approach that uses Stacked Denoising Autoencoder (SDAE) to identify genes that can effectively differentiate between tumor and healthy cases of breast cancer was proposed. They tested the efficacy of the extracted features using an artificial neural network (ANN), SVM, and SVM-RBF. The results showed that using the SDAE method with SVM-RBF achieved the highest accuracy of 98.26%.As earlier noted, several machine learning, deep learning, and metaheuristic techniques have been created and applied to detect and classify cancer using gene expression data. Yuan et al.22 analyzed BRCA gene expression data from TCGA and GEO using differentially expressed genes (DEG) and weighted gene co-expression network analysis (WGCNA) to select the most significant genes. Twenty-three hub genes were then identified using a protein–protein interaction (PPI) network. They applied SVM, decision tree (DT), Bayesian network (BN), ANN, and convolutional neural network (CNN-LeNet and CNN-AlexNet), and the results showed that ANN has the best performance with an average accuracy of 97.36%. Elbashir et al.41 developed a lightweight CNN model for detecting breast cancer using RNASeq gene expression data. They first pre-processed the data by removing outliers, normalization and filtration. Then they converted the gene expression profiles into 2-D images. Thereafter, they applied a lightweight CNN model for the classification. From their result, their model achieved an accuracy of 98.76. Alshareef et al.22 proposed a deep learning model with an artificial intelligence-based feature selection method for prostate cancer detection (AIFSDL-PCD) using gene expression data. In addition, a feature selection (FS) method based on a chaotic invasive weed optimization (CIWO) to select the optimal genes revealed the novelty of their approach. Their results showed sensitivity, specificity, precision, F1-measure, and accuracy of 97.25%, 97.25%, 0.967%, 97.14%, 97.28%, and 97.19%, respectively. Chakraborty et al.32 presented a metaheuristic method for skin disease classification based on an artificial neural network. Their proposed method, a non-dominated sorting genetic algorithm—II (NNNSGAII), was used to train an ANN. The proposed method obtained 87.92% accuracy, 94.2% precision, 87.5% recall, and 90.73% F-measure.Jia et al.33 used metaheuristic methods, including world competitive contest (WCC), league championship algorithm( LCA), GA, particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization algorithm (HTS), Forest optimization algorithm (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK), with an SVM classifier for breast cancer detection using mRNA and micro-RNA expression data. The proposed algorithm selected 186 mRNAs out of 9,692 and 116 miRNAs out of 489 and obtained an accuracy above 90% for the miRNAs dataset and 100% for the mRNA dataset. Wei et al.42 proposed a generative adversarial model based on cancer genetic data (GANs). They used 12 different gene expression data from the TCGA, including lung, breast, prostate, colon, gastric, liver, rectal, esophageal, thyroid, clear cell renal cell carcinoma (CCRCC), uterine, and head and neck squamous cell carcinomas (HNSCC). They further used a reconstruction loss to enhance stability during model training. From their results, an accuracy of 92.6% was achieved by their proposed model. Deng et al.43 proposed a gene selection model in a two-stage format for cancer classification in microarray datasets. Their approach combined a multi-objective optimization genetic algorithm (XGBoost-MOGA) with gradient boosting (XGBoost). During the first stage, the XGBoost-based feature selection is used in ranking the genes to eliminate genes that are not relevant effectively, thereby leaving a group of genes that are most relevant to the class. In the second stage, a subset of optimal genes from the group of the most relevant genes is identified using XGBoost-MOGA through multi-objective optimization. Based on two widely used learning classifiers, a comparison of the proposed method with other state-of-the-art feature selection methods using two widely used learning classifiers on 14 publicly available microarray datasets was performed. The results demonstrated that XGBoost-MOGA outperformed previous methods in terms of accuracy, F-score, precision, and recall.MotieGhader et al.44, the selection of genes that contribute to the prediction of cancer from gene expression datasets with the highest accuracy based on microarray gene expression was achieved by combining a Barnacles Mating Optimizer (BMO) algorithm with SVM called (BMO-SVM). They evaluated the proposed model using four benchmark microarray datasets, including leukemia1, lymphoma, a small-round-blue-cell tumor (SRBCT), and leukemia2. From their results, the proposed BMO-SVM approach performed better than the other well-known methods, such as Particle Swarm Optimization (PSO), the Tunicate Swarm Algorithm (TSA), Artificial Bee Colony (ABC), and Genetic Algorithm (GA). Devi et al.45 proposed an Improved Whale Optimization Algorithm (IWOA) algorithm for gene selection. The proposed solution used a multi-objective fitness function that balances error rate minimization and feature selection. The results show that the proposed IWOA obtained a minimal subset of genes used for the BRCA classification using Gradient Boost Classifier (GBC) and achieved an accuracy of 97.7%. The related studies are summarised and presented in Table In Houssein et al.From the existing literature, various shortcomings were discovered regarding utilizing deep learning models for the given task. Deep learning models necessitate substantial data, and acquiring sizable, high-quality datasets for analyzing breast cancer gene expression can be challenging. Consequently, this can cause overfitting of the model to the training data, thereby resulting in inadequate performance on fresh, unobserved data. The computational complexity and time required for developing and training deep learning models can pose a significant hurdle to their widespread implementation in clinical practice. The complexity of breast cancer, which entails numerous biological processes such as cell proliferation, invasion, and angiogenesis, may not be captured entirely by deep learning models, thereby restricting their capacity to forecast outcomes or recognize potential therapeutic targets precisely. To resolve this challenge, optimizing the CNN model becomes necessary using suitable approximate optimization methods. Metaheuristic optimization algorithms have been applied to solve these problems. Nevertheless, the critical challenge of using deep learning models for effectively and efficiently classifying breast cancer remains unresolved. Therefore, this paper aims to enhance the efficacy of DL models on breast cancer detection and classification using gene expression data by leveraging a new optimization algorithm inspired by the biological mechanism of the Ebola disease.46. The BRCA contains 1208 clinical samples and 14,895 genes or features. Moreover, there are 113 and 1095 normal and tumor samples, respectively. The data were identified to be noisy with many features. Therefore, different pre-processing steps were implemented to get clean data with genes positively contributing to BRCA detection. To identify the outliers samples, the array-array intensity correlation (AAIC), which defines a symmetric matrix of Spearman correlation between samples, was calculated47. The cut-off value of 0.6 was used to define the outlier samples to remove them. Normalization was applied for the gene expression data to ensure the validity of the expression levels and avoid biases in the analysis48. The TCGAanalyze-Normalization function was used from the TCGAbiolinks library to perform the normalization. Then filtration was performed using a cut-off value of 0.25 for reduction of gene number through the selection of genes whose mean expression values are higher than the cut-off value49. Consequently, the pre-processing obtained a dataset that contains 1208 clinical samples with 14,895 genes.Using the R software, we used the BRCA gene expression data from the Cancer Genome Atlas (TCGA) repository. The GDCquery function from the TCGAbiolinks library was used in developing the query50. Moreover, we transformed the images into grayscale using the cvtColor function from the OpenCV library in Python. This was done to ensure that the images met the requirements of the classification model and to improve image quality. Once the images were converted, they were prepared as input for the hybrid model. Figure The gene expression data was reshaped from 1 to 2D images with a dimension of 122 × 123 to be appropriate for our metaheuristic models. The BRCA gene expression data contains columns that could not be reshaped into the desired dimension. However, 112 columns of zeros were attached at the end to adjust the image sizeAfter the pre-processing step, the resulting images were used as input to the model. A specially designed CNN was used for the optimization model. The architecture of the proposed CNN model is a deep neural network designed to analyze and classify gene expression images with dimensions of entclass1pt{minima34 was adopted in the design of the algorithm. To update the propagation, the EOSA model used a dynamic mechanism for propagation via susceptible, infection, quarantine, recovered, and hospitalized operations to gain a better fit. It helped to find the best or worst solution and provided an intuitive outcome. In this paper, the EOSA metaheuristic algorithm was hybridized with CNN to improve the performance of the CNN model. This was accomplished in all the iterations when the metaheuristic algorithm was trained to achieve the solution vector and update the CNN model. The weights and biases for the CNN were updated, and the loss function was subsequently calculated. Thereafter, the results obtained were compared with different hybrid models. The following steps describe the EOSA-CNN Model:Set up the initial scalar and vector quantities for parameters and individuals, respectively. Assign initial values to individuals categorized as Susceptible (S), Infected (I), Recovered (R), Dead (D), Vaccinated (V), Hospitalized (H), and Quarantine (Q).Randomly select an individual from the susceptible individuals as the index case , create individuals that are newly infectedii.The newly generated cases are then added to the newly infected individuals created in I.Update the position of each susceptible individual based on their displacement, and generate newly infected individuals (nI) accordingly. Note that the greater the displacement of an infected case, the higher the infection rate, with shorter displacement representing exploitation and longer displacement signifying exploration.Evaluate the number of individuals to be added to R, H, D, Q, V, and B determined by the size of I, based on their rates, respectively.Use nI to update I and S.Choose the current best from I and compare it with the global best.While stopping criteria are not satisfied, return to step 4.While there is at least one infected individual and the number of iterations is not complete,Return all solutions and the global best solution.Ebola is a viral hemorrhagic fever that affects humans and primates, also called Ebola hemorrhagic fever or Ebola virus disease. The Ebola viruses cause this disease, which can cause individuals to transition between susceptible, quarantined, infected, recovered, hospitalized, and deceased subpopulations in a seemingly random manner. Drawing inspiration from the Ebola virus's ability to spread effectively, a novel optimization algorithm that is both bio-inspired and population-based was developed. The method of the propagation of Ebola disease (EVD)The pseudocode in Algorithm The displacement scale factor of individuals is represented by entclass1pt{minimaing Eqs. .2\documeThe exploration stage of the EOSA involves the infected individual moving beyond the normal neighbourhood range, The algorithm also uses Eq.  to generTo determine the current best , susceptible (S), Infected (I), Recovered (R), Vaccinated (V), Dead (D), Funeral (F), Exposed (E), and Hospitalized (H) individuals as in Eqs. , 7), , , 10), ,  and 12)12).6\docEquations , 7), , , 10), ,  and 12)12) \docuδ in Eq.  represenThe experiments were conducted using Dell Optiplex 5050 computer machine with the following configuration: an Intel Core i5 7th generation processor with a hard disk size of 500 GB and 16 GB memory. All the models were developed using Python. EOSA-CNN model's performance was compared to that of a standalone CNN and five other metaheuristic algorithms, namely MVO-CNN (Physics-based), GA-CNN (Evolutionary-based), LCBO-CNN (Human-based), WOA-CNN (Swarm-based), and SBO-CNN  indicates the number of images incorrectly predicted as cancerous when they are not, while the true positive (TP) denotes the number of accurately classified cancerous images. False negative (FN) represents the number of cancerous images that were misclassified as non-cancerous. True negative (TN) is the number of accurately classified non-cancerous images. The performance metrics are calculated using the formulas involving TP, FP, FN, and TN presented in Eqs. , 14), , , 16), , , 18), ,  and 20)20).13\doTable The comparative study of the proposed method with five metaheuristic algorithms and CNN is reported in Fig. Figure Figure Figures 21, Jia et al.22, and MotieGhader et al.33. While Elbashir et al.41 achieved higher classification accuracy than our study using a CNN model, our approach showed a sensitivity of 0.9890% and an f1-score of 0.99% for both tumor and normal class. Moreover, the EOSA-CNN model achieved a sensitivity of 0.989%, which means the model has missed a few of the positive cases. Sensitivity is a crucial metric as it assesses the model's ability to detect positive cases correctly. Our models must identify all positive cases to ensure accurate predictions. Thus, this study highlights the significance of employing a metaheuristic algorithm to optimize CNN model hyperparameters, which is crucial in selecting the optimal combination of biases and weights required to train a CNN model effectively. Furthermore, the proposed method showcased that integrating these methods can significantly enhance gene expression data's overall performance and classification accuracy.Table In this section, the limitations of the study are discussed in more detail, including the small sample size of gene expression data compared to the very high number of genes. Moreover, the absence of addressing the problem of imbalanced data using approaches such as random over and under-sampling and cluster-based over-sampling is considered a serious challenge. The sample size used for the study may not be sufficient to capture the full complexity of the gene expression data, leading to potential biases and limitations in the analysis. Additionally, the issue of imbalanced data can significantly impact the model's performance, as the algorithm may be biased towards the majority class and struggle to predict the minority class accurately. While the EOSA-CNN model outperformed traditional CNN models and other hybrid algorithms, there is still room for improvement in addressing these limitations. Future research should concentrate on more experiments using large sample sizes of genomics data with handling class imbalance to enhance the model's effectiveness. Despite this constraint, the EOSA-CNN model outperformed other hybrid algorithms and traditional CNN models. Furthermore, evaluating the EOSA algorithm's performance in diverse diseases and medical conditions would be crucial to assess its generalizability and applicability to a broader range of healthcare problems. By addressing these limitations and exploring the model's performance in various contexts, the EOSA-CNN model could be a promising tool for accurate and reliable disease diagnosis and classification based on gene expression data.Breast cancer is the most common medical diagnosis in women. The study, understanding and research of breast cancer have aided the diagnosis and development of new treatments for breast cancer. Gene expression profiling is helping researchers and doctors to comprehend the heterogeneous nature of breast cancer on a genomic level. In this study, we developed a hybrid model that combines the Ebola optimization search algorithm (EOSA) with CNN architecture for the detection of breast cancer and diagnosis using gene expression data. We prepared the data using different pre-processing methods, including removing the outliers using Array-Array Intensity Correlation (AAIC). To avoid biases in the expression measures, we utilized the normalization method. The final step in pre-processing was filtration. After that, we converted the gene expression data into two-dimensional images, which were converted into grayscale images. For the classification, we use the EOSA-CNN model. The findings of this study demonstrate that the proposed model achieved high-performance measurements with exceptional accuracy (98.3%), precision (99%), recall (99%), f1-score (99%), kappa (90.3%), specificity (92.8%), and sensitivity (98.9%) for the cancerous class. These results suggest that the model has the potential to be an effective and reliable method for breast cancer detection using gene expression data. For future extensions, we planned to solve the problem of imbalanced data and hybridize the model with various state-of-the-art optimization algorithms.


A priori graph constraints have been used in the literature as a way to improve feature selection in the model, yielding more interpretability. However, it is still unclear how these graphs interact with the models and how they impact the feature selection. Additionally, with the availability of different graphs encoding different information, one can wonder how the choice of the graph meaningfully impacts the results obtained.In the field of oncology, statistical models are used for the discovery of candidate factors that influence the development of the pathology or its outcome. These statistical models can be designed in a multiblock framework to study the relationship between different multiomic data, and variable selection is often achieved by imposing constraints on the model parameters. We proposed to study the graph penalty impact on a multiblock model. Specifically, we used the SGCCA as the multiblock framework. We studied the effect of the penalty on the model using the TCGA-LGG dataset. Our findings are 3-fold. We showed that the graph penalty increases the number of selected genes from this dataset, while selecting genes already identified in other works as pertinent biomarkers in the pathology. We demonstrated that using different graphs leads to different though consistent results, but that graph density is the main factor influencing the obtained results. Finally, we showed that the graph penalty increases the performance of the survival prediction from the model-derived components and the interpretability of the results.https://github.com/neurospin/netSGCCASource code is freely available at  The decreasing cost of biological data acquisition technologies has made high-throughput multimodal databases for hundreds of patients publicly available to study clinically relevant problems, especially in oncology. It includes multiomics data such as gene expression profiles, mutation profiles, copy number variants (CNVs), clinical data, imaging data, etc. These different data may be considered as blocks for which each line includes numerous measures for an individual. In a multiblock experimental setting, each individual of all the different blocks are paired across the blocks. This led to the development of new multiblock machine-learning and statistical models designed to integrate data from various sources on the same observations. These statistical models are not only used to predict the outcome of diseases but also to characterize them by finding a set of variables of interest explaining their outcome. However, isolated variables associated with a pathology outcome usually do not by themselves give a biological meaning to this association. For example, at the gene scale, somatic mutation profiles from patients with the same pathology may differ from one to the other because different genes are mutated in the same pathway  model , namely a priori knowledge. For example, it would be desirable to favour the selection of a group of genes interacting with each other to exhibit biological functions and pathways of interest. This would give a more interpretable solution than selecting isolated genes. In this perspective, other variations to the LASSO have been proposed to group the variables, such as the fused LASSO   penalty ed LASSO , and groed LASSO . Howevera priori graph of variable interactions has been proposed to improve the grouping effect and makes it reflect a priori knowledge. a priori graph of interactions between variables as an extension of the ridge penalty. They used the normalized graph Laplacian. Their study exhibited a grouping effect property similar to that of Elastic-Net and the capacity of the penalty to find subnetworks in the context of gene expression data. In parallel, The injection of the The GraphNet penalty has been successfully integrated into multiple models aimed at variable selection . We used the data as they have been made available on https://www.openml.org/ by For this article, we worked with the Low-Grade Glioma (LGG) dataset from The Cancer Genome Atlas (TCGA) project  to these three blocks, as proposed by Different gene–gene interaction graphs with different properties were used. Pathway Commons v12 (PC)  is also We aimed to compare the gene selection abilities of the different graphs. First, using the PC graph, we established the differences between the raw and normalized graph Laplacian, in terms of the number of selected variables, as the In order to achieve the experimental design, we first isolated 15% of the patients (63 patients) as a test set and performed the analysis on the remaining 356 patients forming the training set. We stratified the patients using the event status. We used 100 bootstrap samples without replacement, each containing 85% of the training set. Based on these samples, we performed 100 runs that allowed us to assess the stability of the method and provide the mean to study the distribution of the different metrics used. The test set was only used for the survival prediction in the final part of our study.j and k, with c-index – on a 5-fold (using the training set) grid search using SGCCA.Throughout this work, To compare the normalized graph Laplacian with its raw version, using the PC graph, we varied the In each of the 100 runs, we examined the degree distributions by considering the subset of selected genes in the full PC graph on the one hand and, on the other hand, in the PC subgraph containing the selected genes. We looked at netSGCCA gene weight distribution according to the degree of the gene in the PC graph. Finally, the model selects genes by correlation, which is exhibited in Since the normalized graph Laplacian allowed the selection of more stable gene sets, the following experiments will use only normalized graph Laplacians. We ran our model using the MSIGDB and KEGG graphs on the same 100 bootstrap samples and the same model hyperparameters. We compared the set of selected genes from each sample when using netSGCCA with the normalized PC-graph Laplacian and the normalized Laplacian of the other graphs see . To do sTo better exhibit the influence of the graph semantic (information embedded in the graph), we randomly permuted gene labels within the PC graph. This was done ten times to check the validity of the results. We compared, through the Dice metric, the different selected gene sets using the original PC graph and its permuted version for each bootstrap sample. The different permuted graphs selected a similar number of genes as the original PC graph, but as shown in To show the impact of the graph density on the number of selected genes, we removed edges randomly from the PC graph. We made sure that, for each bootstrap sample, we removed edges from selected genes using the normalized PC graph in the same proportions as from the whole edge set. We investigated the importance of the direct and the indirect paths between the genes selected by a model with the GraphNet penalty based on the full PC graph. To do this, for each sample, we removed inner edges (edges connecting two selected genes by the PC graph), outer edges (edges connecting a selected and nonselected gene), and all edges of the selected nodes, which would make them completely isolated. As shown in c-index scores compared with the model without a graph. When using the MSIGDB and KEGG, results are slightly better compared with the other models on the validation and test sets. This indicates that the GraphNet penalty did not weaken the ability of the estimated components to predict patients’ survival. However, the model without GraphNet could not reliably extract genes, which hindered its ability to identify pathways of interest. SGCCA, without a graph, selected about three variables per fold. It is in line with the results found so far, as the c-index noticeably, which is also expected as the variable selection has shown a substantial overlap. Adding the graph made the signatures from other blocks less stable. The more stable mRNA variables are selected, and the fewer and less stable variables are observed from the CNV block. As a result, no stable variables are obtained from the CNV block when a graph was used, but it must be balanced by the fact that for each fold, the model selected about 150 variables when the normalized graph Laplacian was used (compared to 250 when no graph was used).Using the components extracted from the CNV, mRNA, and the miRNA, by the netSGCCA method, we performed a survival prediction using a simple Cox model. We compared the results using the different graphs and a model without a graph constraint, and we present the results in Enrichr platform . Finally, the overlap between selected variables in the penalized block when using graphs with equivalent density but with different semantics, demonstrates the netSGCCA model relies on the graph.From the application of netSGCCA to a real multimodal oncological dataset, we have derived general observations. For the block being submitted to the GraphNet penalty, the similarity of the variable profiles is the primary driver for coselecting variables, and the proximity of the variables (as nodes) in the graph is secondary. The density of the graph c-index values were obtained without using the medical data . The results found are similar to reference values reported in a recent work which did consider medical data regulated in other studies of low-grade gliomas and other brain tumours. Future applications should focus on extending the results to other tumour types. Additionally, the multiblock model should enlarge the scope of multimodality beyond molecular data. New data sources can be investigated, such as imaging data with their specific penalty resources, in order to increase prediction performance or unveil shared information between modalities.btad454_Supplementary_DataClick here for additional data file.


Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods . QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872. Single-cell RNA sequencing (scRNA-seq) generates the expression profile of transcripts for every single cell in a given population , and proThe advancements in single-cell sequencing technologies and protocols now enable millions of cells to be sequenced . HoweverA number of gene selection algorithms and tools have been introduced in the literature for scRNA-seq cell type identification. Among the aforementioned three feature selection approaches, the filter-based approach is the most widely used  in findiBesides filter-based methods, a number of embedded-based gene selection methods are also available for scRNA-seq cell type identification. For example, RFCell  uses datk-nearest neighbours (KNN) classifier wrapped by a genetic algorithm is used in this work. FSGA also emphasizes the biological relevancy of the selected features.In the literature, the number of tools that are developed in the wrapper approach to perform scRNA-seq gene selection is relatively few. Information gain ratio and genetic algorithm with dynamic crossover (IGRDCGA)  is one oDespite superior performances of these methods, to the best of our knowledge, there is a lack of benchmarking studies utilizing wrapper-based gene selection methods in the literature. The wrapper-based methods have shown promising results in other fields –41; howeThe rest of the content in this paper is divided into several sections. The “Materials and Methods” section explains the datasets, gene selection methods, and experimental workflow applied in this study. The “Results” section compares the performance of different feature selection methods in the experimental study, while the “Discussion” section presents the analyses and findings from the study. Finally, the “Conclusion” section summarizes the work.Twelve popular scRNA-seq datasets from the public portals are used in this study. These datasets comprise the records of single cells from various cell types or cell cycle stages. The twelve datasets selected for this study contain single cells from human and mouse samples. They can be divided into three broad categories, namely developmental, metabolic diseases, and connective tissues. Four datasets from human  and mousIn this study, a minimal preprocessing is applied to the data by excluding cells with ambiguous labels such as “None/Other” in Pancreas_Human_Lawlor_2017; “not applicable”, “unclassified endocrine cell”, “unclassified cell”, and “co-expression cell” in Pancreas_Human_Segerstolpe_2016. The absolute or normalized gene counts are retained as in the original dataset sources. No other normalization techniques have been applied to the data with a purpose to evaluate the performance of the gene selection method in selecting gene features from the datasets.In this study, a wrapper-based feature selection by a metaheuristic approach is employed, where QDE is utilized as a feature subset searching algorithm to select the genes that characterize cell types. A classification algorithm is used to quantize the goodness of these gene subsets in classifying cell types. QDE is a metaheuristic algorithm based on differential evolution (DE) that uses quantum computing in the feature subset initialization process . It has D gene features; each candidate solution is made up of D binary bits, where a state of 1 indicates that the corresponding feature is selected as a candidate feature, and a state of 0 indicates that it is not included in the solution. The state of each feature is determined by observing a quantum bit. This quantum-based initialization process is adopted from the work by Srikrishna et al. [The QDE feature selection process begins by initializing 30 feature subsets (candidate solutions), which is the same setting as mentioned in Srikrishna et al. . Assume a et al. . The firF1 is the F1-score of the feature set, N is the number of selected genes, and D is the total number of genes before selection.The fitness (goodness) of these 30 candidate solutions in representing cell types will be evaluated using a classifier. In this research, five machine learning (ML) classifiers are employed to determine the most suitable classification model for handling scRNA-seq data. Four of them are coded using the scikit-learn library , namely F1i is obtained from the mean F1-score of the initial population with an addition of 0.1 to allow improvement in the classification performance. Ni is an adjustable variable for the ideal portion of features to be selected. It is set to 0.01 throughout this study, i.e., 1% of the total number of features, to determine the best-performing feature subset.Mutation and crossover of the population are performed before forming a new population. This process creates child solutions in which their performance is then compared with their respective parent solution. The mutation and crossover are performed at a rate of 0.8 out of 1 to explore for better candidate solutions in the large feature subspace while still exploiting the current solutions . After gThe process of feature subset evaluation, mutation, crossover, and selection is continued until reaching a specified number of generations. Since all datasets have a large number of features, the QDE is executed for 100 iterations so that the evolution process is carried out not too long to avoid overfitting results. The flowchart of the classification-based QDE is depicted in The experiments are designed in two stages using a 5-fold cross-validation strategy: (1) in the first stage, the best classifier for wrapper QDE is determined; (2) in the second stage, the selected method from stage 1 is used to compare with the classification performance of the recent methods comprising different feature selection algorithms.In the first stage, the experiment is conducted using QDE wrapped with five different classifiers using the hyperparameters settings listed in The second stage of the experiment is a comparison of classification performance between the best-performing QDE method chosen from the first stage and four recent methods with different feature selection algorithms , 65–67 tOne of the recent methods is FSCAM . FSCAM aApart from FSCAM, another three classification-based wrapper methods, namely hill-climbing-based social ski driver (SSD-LAHC) algorithm , mayfly-When observing the time taken to complete the feature selection process , QDE witα = 0.05 was used. The p-values of all tests listed in Before selecting the best QDE method, statistical test was performed. Since the results are not normally distributed, where they are largely skewed from the mean value, a non-parametric statistical test was applied. Friedman test  was chosTo identify how the methods are different from each other, post hoc tests were conducted using Holm’s procedure  for pairThe results in The method introduced in this study is further compared with recent wrapper-based methods, including a clustering-based wrapper method , and three classification-based wrapper methods  was extracted from the five-fold candidate solutions of each method. For each method, the gene subset with the highest fitness score (as defined in Eq ) was idehttp://geneontology.org/ to validate the biological significance of the selected gene subset. The test and correction methods used for the enrichment analyses were Fisher’s exact test and false discovery rate (FDR). For each gene subset, the top 15 enriched GO terms were determined from terms of third-level and above in the ontology, as well as terms with high gene ratios and low FDR. For each category of the datasets , a dataset with a moderate number of genes was chosen as the representative dataset for analysis .As the genes selected by the five methods are different, the biological significance of the selected gene subsets is further discussed after conducting a gene enrichment analysis. Gene Ontology (GO) enrichment analysis was performed at 10FDR, where the lighter color shows lower FDR.For the embryo development dataset (Embryo_Mouse_Biase_2014), the gene subset selected by QDE-SVM is enriched in developmental-related terms such as Golgi vesicle transport, which are essential for embryo development . SignaliGO enrichment analysis shows that the genes selected by QDE-SVM are biologically relevant. The top 15 terms are not similar to the one in FSCAM, which is expected as the overlapping genes are few. Both methods could be useful in discovering biologically relevant genes. Overall, this study provides a brief functional analysis of the gene features selected. More practical efforts are needed to validate the usefulness of the GO terms in downstream applications such as biomarker design.QDE-SVM is potentially useful in various applications. While serving for reducing the scRNA-seq data dimension, the selected gene features can also be applied for future cell-type classification tasks in similar experimental settings . As not The classification component in QDE-SVM gives a slightly different observation of results from the clustering-based method. It is noticed that classification-based methods  generally select more accurate gene subsets than the clustering-based method . This is reasonable as they are supervised algorithms provided with labeled cells. However, the number of genes selected by QDE-SVM is only around half of the original number of genes, which might still be further reduced for feasible downstream applications. Also, when comparing different feature selection categories, an obvious limitation of the wrapper-based method is the time needed to conduct feature selection. The well-known non-wrapper-based methods such as Seurat  or scmapIn the future, the first effort should be taken to reduce the number of genes while still preserving the superior classification performance of QDE-SVM. It can be done using threshold-based filtering steps as in FSCAM or using other filtering methods such as information theory, distances, correlation, etc. This would require additional studies and experiments to determine the suitable filters. Another possible future work is to improve QDE using different schemes of mutation and crossover  or diffeIn conclusion, a classification-based wrapper method for scRNA-seq gene selection has been presented. A linear SVM wrapped with QDE was suggested in this work based on the feature selection results on twelve well-known scRNA-seq transcriptomic data. QDE-SVM has been tested and validated to select biologically relevant gene subsets with superior cell type classification performance when compared to QDE wrapping with other classifiers and the recent wrapper methods. However, QDE-SVM has a limitation when compared to a recent wrapper-based scRNA-seq gene selection method, FSCAM. The number of features being selected by QDE-SVM could still be reduced to obtain a set of informative marker genes for effective downstream analyses. Nevertheless, given the higher accuracy achieved by QDE-SVM in a similar time required for both of the wrapper methods, QDE-SVM is suggested as a promising gene selection method for further exploration.


Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA), enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection. With 1.8 million deaths per year, lung cancer remains the leading cause of cancer mortality worldwide ,3,4,5. NIn recent years, liquid biopsy (LB) has been proposed as a highly promising diagnostic approach for the detection and management of cancer patients. An analysis of tumor-derived biomarkers present in human body fluids offers a minimally invasive, safe, and sensitive alternative or complementary approach to tissue biopsies. Besides the commonly used blood-based biosources and biomolecules, such as circulating tumor cells (CTCs), cell-free DNA (cfDNA), and extracellular vesicles (EVs), blood platelets have recently emerged as promising novel carriers of cancer biomarkers ,7,8. PlaPlatelet RNA repertoire includes several types of RNA families which can be potentially used as biomarkers. A first insight of the diagnostic potential of the TEPs transcriptome was described during the profiling of the platelet mRNA repertoire of metastatic lung patients and asymptomatic individuals. This study discovered that the presence of cancer results in altered spliced mRNA profiles . AfterwaMore recently, the expression of other types of RNAs has been found dysregulated in platelets ,23. In pPrevious studies on platelet transcriptome were based on the use of RNA-seq technology. Although RNA-seq is currently the most used methodology for genomic-based biomarker discovery, its implementation in the clinic has several limitations due to its time-consuming and elaborate library preparation protocol, the lack of standardized methods, the high cost, and complex data analysis .NanoString nCounter, a platform for the high-throughput analysis of gene expression, has grown in popularity both in clinical settings and in translational research due to its fast, simple, and reliable protocol. By directly hybridizing and counting the individual targets, nCounter technology enables the multiplex analysis of signatures up to 800 genes with high reliability and reproducibility. In contrast to RNA-seq methods, nCounter RNA analysis does not require reverse transcription, amplification, nor cDNA library construction. Altogether, all these features make this system less prone to bias, leading to a more accurate quantification of the targets. Clinical tests have been developed employing nCounter technology, including the FDA-approved nCounter Prosigna test, which stratifies breast cancer subtypes and predicts recurrence risk in post-menopausal women ,28, and Here, we present the development of a protocol for the interrogation of platelet mRNA and circRNA repertoire using NanoString nCounter technology and machine learning (ML) approaches. We applied this methodology to the platelet transcriptome obtained from lung cancer and non-cancer individuals in order to identify and evaluate the diagnostic value of each of the individual mRNA and circRNA signatures. Since a single type of biomarker may lack sensitivity and specificity for the enrichment of reliable clinical diagnostics information, we also explore a multi-analyte-based approach, using a combinatorial analysis of platelets-derived mRNA and circRNA to improve the detection of lung cancer.2 = 0.99, p-value < 0.0001, both for cancer and control samples, 2 = 0.98, p-value < 0.0001, for control and R2 = 0.97, p-value < 0.0002 for cancer, We investigated if a direct platelet RNA analysis might provide adequate gene expression information without performing any pre-amplification step. Due to the limited amount of total RNA present in platelets, we tested different concentrations to determine the minimum amount of RNA input necessary to preserve critical gene expression information. Six different RNA concentrations  obtained from platelets of a lung cancer patient and a non-cancer individual (indicated as control) were analyzed by using the human immunology v2 panel  37]. As. As37]. However, we found a significant drop in the number of transcripts detected when 1 ng and 3 ng of total RNA were used compared with higher concentrations . Using PTherefore, we conclude that a minimum concentration of 6 ng of platelet RNA without pre-amplification process is recommended for sufficient and robust transcripts expression profiles for platelet-RNA analysis with nCounter.n = 30) and non-cancer controls (n = 30) equally distributed per age and gender (n = 20) while the remaining samples were from patients diagnosed with metastatic tumor stage (n = 10). We include both asymptomatic individuals (n = 27) and samples from patients with confirmed benign lung nodules (n = 3) in the control group. Total RNA extracted from platelets was stored in RNAlater (as explained in Following the protocol described in d gender . Since eAfter subtracting the background (negative control) signal, we observed that 159 out of the 594 genes in the human immunology v2 panel were not present in any of the processed samples. A total of 402 platelets-derived mRNA were detected in both the control and cancer groups, whereas 18 transcripts were exclusively found in the control group and 15 in the lung cancer group a.All the 78 circRNAs present in the custom-made panel were detected in at least one of the samples. Only three circRNAs  were exclusively detected in the lung cancer group b. All thp-value > 0.05)  c.p-value > 0.05)  d.mRNA raw count data  was eval2 = 0.002 .p-adj < 0.05), of which 15 were upregulated and 10 downregulated in lung cancer patients . Differp < 0.0001, To evaluate the potential use of the human immunology v2 panel as platelet signature for lung cancer detection, we employed ML approaches . The RFECV algorithm selected a final 28 mRNAs signature . To invep < 0.0001, The same ML approach was applied to investigate the diagnostic potential of the 78 circRNA custom panel. The RFECV method selected a signature of 21 circRNAs . Both RFCombinatorial analysis of different types of molecular biomarkers has not yet been investigated in platelets. Our unique cohort of samples allows the exploration of both platelet mRNA and circRNA derived from the same source.p-value < 0.0001, Using the same ML approach applied before, we built a new predictive model using features derived from both the mRNA and circRNA panel  and excluding the two previously identified outlier samples . The RFEIn terms of AUC, accuracy, and specificity, this model outperforms the results seen in the previous models using an independent signature of mRNA or circRNA, suggesting a potential synergistic role of the combinatorial use of these two RNA types as molecular biomarkers.n = 30)  together with the control cohort (n = 30) –C. The cn = 30) . The senn = 30) , the claCumulatively, our data strongly suggest that combinatorial analysis of different RNA types found in blood platelets enables optimal classification of lung cancer patients and demonstrates the potential for early-stage detection.Platelet transcriptome is a rich source of cancer biomarkers. In this study, we developed a novel and reliable methodology for the interrogation of platelet mRNA and circRNA repertories in order to discover and assess the diagnostic value of each individual RNA type. However, most current liquid biopsy tests rely on the use and analysis of one single type of molecular biomarker, which may often lack the sensitivity and specificity required to obtain clinically reliable information. Therefore, we investigated whether combinatory analysis of platelet mRNA and circRNA derived from the same source may help us to improve the detection of lung cancer patients compared to using the single signature of both types of biomarkers.Most of the current studies on platelet transcriptome have been based on RNA sequencing data. Although RNA-seq represents a powerful tool to perform high-throughput analysis, its clinical use is limited by the long turnaround time, high cost, and the complex computational analysis. NanoString nCounter technology represents a valid alternative for the clinical implementation of LB tests. Different from the qPCR and NGS assays, this methodology permits a robust and reliable quantification of the RNA molecules without the bias introduced by reverse transcription or amplification. The automated processing minimalizes in-between steps handling errors. The time from sample preparation to data results requires only three days. However, this technology has not yet been largely utilized for liquid biopsy profiling.Clinical samples, specifically liquid biopsy specimens, often suffer from a limited amount of RNA material for subsequent gene expression analysis. We investigated whether direct usage of platelet RNA in the analysis could provide adequate gene expression profile with the least amount of input. Our findings led us to the conclusion that no pre-amplification step is required to assess gene expression in platelets from as little as 1 ng of total RNA. However, a minimum of 6 ng of RNA is recommended as initial input to reduce intrasample variability and increase the reproducibility of the assay.n = 30) and non-cancer individuals (n = 30) were investigated using two different gene panels. The human immunology v2 panel includes 594 genes involved in the immune response such as cytokines, enzymes, interferons, and their receptors [In this proof-of-concept study, mRNA and circRNA profiles of human platelets derived from lung cancer patients .In the second and third parts, we used and assessed two different biostatistical packages for normalization and DE analysis. Based on RLE plot analysis, DESeq2 outperformed edgeR normalization for both panels studied. DE analysis of the mRNA panel resulted in a total of 25 DE mRNA b. AccordThe fourth section of this dry lab workflow employs machine learning approaches to generate prediction models. ML can be considered a novel method for developing predictive signatures that typically outperforms individual biomarkers identified by differential expression analysis.Using individual mRNA and circRNA data profiles, the ML prediction models generated reached an AUC of 0.88 using a selected signature of 28-mRNA and an AUC of 0.81 using a 21-circRNA signature a,c. Howen = 3) and stage IV (n = 3). This implies that the selected biomarkers from our prediction model most likely reflect the gene expression signature of the earlier stages of the disease. This hypothesis was further supported by the combinatorial analysis performed only with samples diagnosed as surgically resectable tumors (stages Ia–IIIa). This model, indeed, confirmed that five out of the eight biomarkers previously selected  generated a predictive model specifically for early-stage cancer detection reaching an AUC of 0.96, sensitivity of 85%, and specificity of 86% , asymptomatic individuals (n = 27) and people with benign lung nodules (n = 3) were provided by the Amsterdam UMC  and Maastricht University Medical Center . Whole blood was drawn at the Amsterdam UMC into EDTA-coated BD Vacutainer tubes. At the Maastricht University Medical Center, BD Vacutainer tubes containing 3.2% buffered sodium citrate were used for blood-sample collection. Both collection protocols guarantee minimal platelet activation [Whole blood samples from lung cancer patients  tumors, one day before surgery. Histological analysis of the tumor tissue biopsy was performed to determine the diagnosis. Asymptomatic individuals had no prior or current medical records of any kind of cancer during the time of blood collection and no additional examinations were carried out to verify the absence of cancer.Clinical information about the patients was gathered, including their age, gender, type of tumor, and level of metastasis . For theClinical follow-up of asymptomatic controls was not available due to the anonymization of these samples in accordance with the ethical guidelines of the hospitals. The Declaration of Helsinki’s guiding principles were followed in the conduct of this investigation. This study has received approval from the medical ethics committees of the two participating hospitals . The informed permission form for blood collection and blood platelet analysis was given to and signed by each participant.g for 20 min, followed by PRP centrifugation at 360× g for 20 min at room temperature. Resulting platelets pellet was re-suspended in RNAlater , incubated at 4 °C over-night, and stored at −80 °C until use.Platelets isolation from the whole blood sample was performed as previously described . Brieflyg for 15 min. PRP was supplemented with iloprost (50 nM) to reduce ex vivo platelet activation. PRP was centrifuged for two minutes at 1600× g to pellet the platelets, followed by the addition of RNAlater and storage at −80 °C until use. Both procedures guarantee the isolation of highly pure platelet pellets with minimal leukocyte contamination and platelet activation. There were no discernible deviations detected in downstream analyses between the two methods [At the Maastricht University Medical Center, PRP was obtained by centrifuging blood sample at 240×  methods ,48,49.Total RNA isolation was carried out using the mirVana RNA isolation kit according to the manufacturer’s instructions . Extracted RNA was eluded in 30 μL of mirVana buffer and the quantity and quality were assessed by RNA 6000 Picochip . RNA samples with RIN values higher than 7 and/or with distinguishable rRNA peaks were considered for further analysis.The assays were performed using the NanoString nCounter Flex System  with two different nCounter panels for the analysis of platelet-derived RNA. The human immunology v2 panel (NanoString Technologies) targets 594 genes involved in the immune response such as cytokines, enzymes, interferons, and their receptors . For eac® Prep Station was used to process the samples. The samples were purified and immobilized in a sample cartridge for data collection, where the target mRNA and circRNA in each hybridized sample were quantified, using the nCounter® Digital Analyzer. Output data in the report code count (RCC) format was exported into the nSolver analysis software (version 4.0.70). The background of each sample was computed using the geomean of the counts of the negative probe  plus two times the standard deviation. Raw counts below the negative background value were excluded from further analysis.The automated nCounterPre-processing and normalization of the data were performed using R (version 4.0.3) and RStudio as graphical interface (version 2022.02.2). The quality of the raw RCC proprietary format data was initially assessed by using the NanoStringQCPro (version 1.22.0) package. Standard control metrics embedded by NanoString, such as imaging, binding density, positive control linearity, and limit of detection, were used to search for any potential outlier samples.Additionally, all samples were also subjected to supplementary exploratory examination, including the principal component analysis (PCA) and inter quartile range (IQR) method for outlier detection. Samples higher than the upper bound (Q3 + 1.5 × IQR) or lower than the lower bound (Q1 − 1.5 × IQR) were excluded from subsequent analysis.p-adj < 0.05) expressed genes between the cancer and control groups.Prior to normalization, negative control probes embedded to each panel were used to filter out targets with poor expression and high background noise. Consequently, the background values were firstly calculated, by taking the mean of each sample’s negative controls increased by two times the standard deviation, and then removed from each sample. Any transcript that indicated a score of less or equal to 0 in more than 75% of the examined samples was excluded from further examination. After these filtering steps, the data was again evaluated using a PCA plot. Two different packages were compared for the normalization of the data: DESeq2 (version 1.30.1) and edgeR (version 3.32.1). The normalization performance was assessed using the standard relative log expression (RLE) plot. DESeq2 was chosen as the default to perform the normalization of the data. Differential expression (DE) analysis was performed to find significantly differentially (|FC| > 0.5 and The machine learning approach was implemented in Python (v3.9.13) using the Scikit-learn (v1.1.0) library. Initially, the DESeq2-normalised data, along with each sample’s classification label, were imported into the python environment. For combinatorial analysis, the mRNA and circRNA normalized datasets were merged together with previous analysis. Highly correlated (higher than 0.95), as well as quasi-constant features, were excluded from further analysis.The recursive feature elimination with cross-validation (RFECV) algorithm was then utilized along with the random forest (RF) classifier to perform the feature selection in addition to the leave-one-out cross-validator (LOOCV). RFECV determined automatically the number and the composition of the most relevant features. This subset of genes, which composes the prognostic gene signature, would further be used as an input to our classification models.Two different supervised machine learning algorithms, RF and extra trees classifiers (ETC), were selected along with the selected features to perform this classification problem. In our case, the 5-fold cross-validation (5CV) was used. In a more detailed manner, the dataset was randomly divided into 5 folds, with 4/5 of the data being used to train the model and the remaining 1/5 being used to test its behavior. This process was repeated 5 times. The use of k = 5 was chosen to reduce the bias in the testing set due to the limited number of samples available. The classifier with the highest mean AUC ROC value was then selected. Probability scores for each sample were obtained from the final classifier. Finally, additional statistical metrics such as sensitivity, specificity, accuracy, PPV, and NPV were also calculated.


Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced.Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores.Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers.Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods. Two major approaches are used nowadays for large-scale transcriptional profiling: microarray hybridization (MH) of mRNA  , drug efMore recently, a new concept was formulated for the harmonization methods: conversion of a whole set of profiles into the shape of a pre-defined experimental platform, e.g., in the Training Distribution Machine (TDM) method . AccordiWe then introduced a new type of uniformly shaped cross-platform harmonizers that use several mathematical transforms . The firCurrent versions of Shambhala utilize transformation of a fraction of ∼8,000 most strongly expressed human genes because their transcriptional activities can be assessed with the greatest precision compared to the low-expressed genes . In our Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, and stability of drug efficiency scores. We also assessed the quality of Shambhala output data for building both local and global machine learning expression-based classifiers of human cancer and normal tissue types. The piecewise-cubic Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs. validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.1) Comparison of different cancer and normal tissue datasets of gene expression profiles obtained using different equipment/protocols. The normalization methods included QN , DESeq2 - the accuracy of the transfer learning classifier, where the ML model is trained on profiles obtained in one batch and validated on profiles obtained in different batches and using different platforms/protocols;- clustering quality of expression profiles after harmonization: “good harmonization” means that such clustering corresponds to biological type of the sample rather than to batch or experimental platform;- correlation and linear regression coefficient between expression profiles for the same tissue type, obtained using different equipment/protocols: should be close to 1 for a good harmonization.2) Analysis of correlation and linear regression coefficients, as well as sign stability for downstream measures of gene expression derivatives  , and dru- different linear and cubic Shambhala modes vs. QN;- LFC, PAL, and BES values calculated using Oncobox ANTE control sampling vs. GTEx control sampling.3) Retention of biologically relevant differences between various types of profiles after harmonization :- differences between the male and female individual samples for the genes located on sex chromosomes,- differences between cancer expression profiles in hormone-dependent, HER2-positive, and triple-negative breast cancer patients.Our validation of the Shambhala method for uniformly-shaped harmonization included the following tests .1) CompaWe curated six gene expression datasets for cancer and corresponding normal transcriptomic profiles, namely, The Cancer Genome Atlas (TCGA) —both forR) dataset. The procedure relies on two preselected auxiliary datasets: the calibration (P) and reference definitive (Q) datasets. Every single profile is taken from the R-dataset, and quantile-normalize  for each gene g in the dataset H as follows: LEg = μgQ+ LEgʺ·σgQ, where μgQ and + σgQ are mean value and standard deviation, respectively, for log-expression level of gene g in the Q-dataset. Second mode termed Q-based rescaling, utilizes setting the mean value, and the standard deviation, for the log-expression levels of each gene in the dataset H, to the corresponding levels of the dataset Q, equal to μgQ and σgQ, respectively: LEg = μgQ+ (LEgʺ − μgH)·σgQ/σgH. The third mode termed R-based rescaling, has the mean value, and the standard deviation, for the log-expression levels of each gene g that are used in the dataset H to the corresponding levels of the dataset R, equal to μgR and σgR, respectively: LEg = μgR+ (LEgʺ − μgH)·σgR/σgH.We used three alternative rescaling modes for Shambhala-2 method. The escaling  utilizesP and Q, we used the dataset P0 (obtained using the MH platform Affymetrix Human Genome U133A 2.0 Array) and Q0 (obtained using the NGS platform GTEx Ilumina HiSeq 2000), respectively. Among other tested P- and Q-datasets, the datasets P0 and Q0 showed the best results in our previous studies  Quantile normalization (QN) , implemeestimateSizeFactors method from the DESeq2 R package, available at https://bioconductor.org/packages/release/bioc/html/DESeq2.html. Although DESeq2 can be used for differential gene analysis, it can also be applied for agnostic normalization of the NGS data, if all profiles are considered as belonging to one batch.2) DESeq2 normalization , implemesva package at Bioconductor code repository: https://bioconductor.org/packages/release/bioc/html/sva.html;3) ComBat, or Empirical Bayes, designed for artificial elimination of the batch effect between two gene expression datasets , availabHARMONY) is available at https://github.com/oncobox-admin/harmony;4) Shambhala-1, or linear Shambhala . The codP-based rescaling (Sh2PBR) of the output data, written as R code that calls a MATLAB function, available at https://github.com/BorisovNM/Shambhala2;5) Shambhala-2  with P-bQ-based rescaling (Sh2QBR) of the output data;6) Shambhala-2 with R-based rescaling (Sh2RBR) of the output data.7) Shambhala-2 with The following six methods were used for harmonization of gene expression data.i.e., biological sample types, was fifteen for the cancer type classifier, and varied from 8 to 20  for normal tissue classifiers. All the classifiers used the Euclidean feature space of log-expression of each gene (for the kNN approach) and of 20 principal components (for the SVM approach), applying three machine learning methods: (i) 11 nearest neighbors (11nn); (ii) first nearest neighbor (1nn); (iii) linear support vector machine (SVM). The SVM calculations were performed using the e1071R package  with the C++ library libsvm .To avoid overtraining, we applied the transfer learning approach to the sample tissue type classifiers. After harmonization of gene expression profiles, we used one group of samples as the training dataset, and another group as the validation dataset . The num0 trajectory that describes the worst  distribution of predefined classes on the dendrogram (WMS) and experimental platform classes (WMP) may be used for evaluating harmonization quality: the higher R means better harmonization quality (We used Watermelon Multisection (WM) method to quantitatively assess the quality of clustering for the expression profiles under analysis according to . This mendrogram . Therefo quality .https://gitlab.com/oncobox/cluster-analysis.WM metric calculation code was implemented in R, available at We used the following protocol for WM metric evaluation . For eacv1 and v2, for the training and validation datasets, respectively. For each of v1 vs. v2 pairs, we calculated the Spearman correlation and linear regression coefficient (k). The value k was the geometric mean over the values k1 and k2. Here k1 and k2, are the linear regression coefficients with and without the offset item b, in the regression models: v2 = k1 · v1 + b, and v2 = k2 · v1, respectively. If k1·k2 < 0, then we set the resulting k value to zero.For each pair of training and validation datasets, we calculated the median log-expression levels for each gene and each sample type. Let us call the median log-expression level vectors for a certain biological type as Cancer-to-normal log-fold change (LFC) of gene expression, pathway activation levels (PALs), and balanced efficiency score of anticancer targeted drugs (BES) were calculated according to . Cancer v1 vs. v2.v1) vs. using different modes of Shambhala: Sh1, Sh2PBR, Sh2QBR, or Sh2RBR (regarded as vector v2);1) BES values calculated with normalization of cancer and corresponding ANTE normal samples  using QNv1) vs. with GTEx normal samples (v2).2) BES values calculated with ANTE normal samples , similarly to the way that we applied to median log-expression values between similar biological types in the training and validation datasets.For each comparison, we calculated the correlation and linear regression coefficient (w) of a significance threshold around zero. If for the i-th component (corresponding to the drug i) of the vector v1, vi1 < − w/2, and, simultaneously, v2i > w/2, or vice versa, then the i-th component is considered sign-changing for the comparison of v1 vs. v2. The resulting percentage of sign-changed values is the ratio of the number of sign-changed components and of the total number of components.Additionally, for these two comparisons, we calculated the percentage of sign-changed BES values as a function of the width (1) For the BES values without correction, marked “as is”;k, marked as “divided by k.”2) For the BES values divided by the corresponding linear regression coefficient Since different modes of Shambhala harmonization could affect the absolute values of LFC, PAL and BES, we calculated the percentage of the sign-changed BES values using two modes.In this study, we tried to characterize the ability of Shambhala approach to provide tissue specific clustering of the harmonized gene expression profiles, and at the same time to retain their characteristic differential gene expression patterns. These points were assessed after co-harmonization of gene expression profiles obtained using two different experimental platforms and four library preparation protocols .We first investigated the ability of Shambhala to support tissue specific clustering of harmonized expression profiles in comparison with the other, non-uniformly shaped harmonization methods. We took human tissue gene expression datasets from the Gene-Tissue Expression (GTEx) repository , The CanP-based rescaling (Sh2PBR); (vi) Shambhala-2 with the Q-based rescaling (Sh2QBR); (vii) Shambhala-2 with the R-based rescaling (Sh2RBR).To compare performance, we did harmonization procedure by using the following alternative methods: (i) Quantile normalization, QN, a gold standard for normalization of the MH gene expression data ; (ii) DEP-, Q-, and R-rescaling modes for Shambhala-2 differ by different mean and standard deviation values, which are applied to set the log-expression levels after the harmonization procedure. Note that all modes of Shambhala reduced the number of genes after harmonization to ∼8,000 most strongly expressed “reaper” human genes (R), and two auxiliaries (P and Q). The best results, in terms of stressing the biological origin of the profiles and banning the artifacts generated by platform/protocol-specific bias, were obtained for the P-dataset Affymetrix Human Genome U133A 2.0 Array (dataset P0) with 8,385 genes  methods to build tissue type classifiers for the harmonized profiles: (i) 11 nearest neighbors; (ii) first nearest neighbor; (iii) linear support vector machine (SVM). We performed 6 ML tests, one with cancer samples, and five with normal samples. In such tests, training and validation groups of samples were taken from different initial datasets .i.e., percentage of correct tissue type predictions in the validation dataset.For each transfer learning test, and each sample type in the classifier, we calculated the accuracy, validation dataset generally corresponded to greater prediction accuracy, although this trend is rather vague (training dataset was even lower (data not shown).In addition, bigger number of samples in the er vague . From thWatermelon Multisection (WM)  . WM enabWMP for the classes corresponding to experimental platforms/protocols , and WMS for the classes corresponding to tissue types. Thus, the ratio RR, the better is clustering according to tissue type in relation to platform-specific bias, and vice versa.We, therefore, calculated WM metrics for the harmonized tissue samples in two major settings: R for WM metrics are provided by low values of WMP, rather than the high values of WMS, and profile clustering after ComBat is not done according to the tissue type . We found that Sh2PBR method results in the correlation and linear regression coefficients very close to 1, thus being the method of choice for harmonization of human normal transcriptomic profiles , which were obtained using QN for Oncobox cancer and ANTE normal gene expression profiles, which is a routinely used protocol for BES calculation in clinical use (v2) which were obtained after Shambhala harmonization of cancer and normal profiles ((i) first, comparison of BES values , which were obtained using ANTE normal gene expression profiles (v2) obtained with the corresponding GTEx ((ii) second, comparison of BES values  values for the regression coefficient k, with k < 1  around zero is one median value for all BES values in all cases and drugs (from–w/2 to + w/2), then less than 10% of BES values change their sign. Changing sign of BES is meaningful because it indicates whether a drug is potentially helpful against a particular tumor (BES positive), or not (BES negative or zero).The values of regression coefficients, ill 1.32 . Likewisill 1.32 . When thi during comparison of two values, vi1 vs. vi2, and a predefined significance threshold width w, if vi1 < –w/2, and, simultaneously, vi2 > w/2, or vice versa. With the growth of the significance threshold, the percentage of sign-changing BES values rapidly decreases. The strongest decrease was observed for the QN vs. Sh2PBR comparison  were close to 1 for the ANTE vs. GTEx normal reference comparison , HER2-positive, and triple negative cancer samples are mixed together for all normalization/harmonization methods . The staThus, our tests with the expression of the X-chromosome genes in male and female cancer patients, and with marker genes for the hormone-dependent vs. HER2-positive vs. triple-negative breast cancers confirmed the best performance of the Sh1 and Sh2PBR modes over other methods tested.Dozens of methods for cross-platform normalization have been developed for the analysis of both microarray hybridization and RNA sequencing gene expression data, yet none of them is currently recognized as an overall gold standard . The majIn our opinion, the current versions of Shambhala have the following major limitations: limited repertoire of normalized human genes  and higher calculation costs per individual profile due to the algorithm complexity. However, Shambhala has the following advantage over other normalization methods: its output is returned in a universal format comparable with all other “shambhalized” human profiles obtained using any experimental platform or protocol. This requires no recalculation of the whole dataset upon the addition of new sample(s). In the current version of Shambhala, the maximum amplification of signal-to-noise ratio was prioritized whereas it resulted in a reduced spectrum of genes in the harmonized output. Thus, we added reliability expression filters  and the Our previous analysis  has demo- Clustering of samples according to their biological features, and not according to technical factors like platform/protocol used;- Close to 1 correlation coefficients for gene expression values in comparison with the initial datasets;- Close to 1 linear regression coefficients for relative gene expression values and their derivatives like pathway activation levels (PAL) and drug efficiency scores (BES) in comparison with the initial datasets;- Stability of sign for logarithmic relative gene expression values like fold-change and their derivatives like drug efficiency score in comparison with the initial datasets.- Conservation or “common sense” biological properties, such as expression levels of sex-related genes, or marker genes between disease subtypes.Ideally, harmonization of gene expression should provide.In addition, taking into account Big Data analytic approaches, another desirable feature is data suitability for various ML methods, e.g., local and global. An ideal harmonization of gene expression data has to generate similar molecular profiles for the samples of similar biological origin, even when obtained using different equipment and protocols. This means that the ML process trained on the harmonized data obtained using one platform and validated on the harmonized data obtained using another platform should show high overall accuracy. Likewise, the profiles of similar biological origin but obtained using different equipment/protocols, should be strongly correlated.k ∼ 0.20÷0.25, which indicates that Sh2PBR causes four-to-five-fold decrease of case-to-control log-fold changes in gene expression levels (We compared performance of four available versions of Shambhala with the gold standard flexible output methods QN and DESeq2 according to twelve analytic criteria reflecting the above considerations . For mosn levels . Howevern levels . Moreoven levels .In


This Research Topic collected papers aimed at integrating different omics data to investigate the causes of human diseases. The multi-omics analysis, in fact, can leverage the advantages of each set of data, collecting more complete biological and clinical information about patients and thus providing a more holistic point of view. Integrating analysis allows us to model the correlations or associations likely present at the different omics layers of related data leading to a better modeling of the complexity of data and the possibility of applying multivariate methods. Ultimately, these approaches can provide hints on the complex biological and molecular patterns underlying human traits and diseases. Several multi-omics approaches have been published in order to improve knowledge about human diseases, whether they are Mendelian diseases , complexhttps://www.cancer.gov/tcga) and GTEx database , and the newly released proteomic dataset by UK Biobank (https://www.ukbiobank.ac.uk/), just to name a few. Among the papers collected in this Research Topic, Chen et al. investigated the SHC-adaptor protein 1 (SHC1) role in cancer through a pan-cancer analysis taking advantage of data from public databases . The SHC1 expression levels were examined with regard to several crucial issues related to cancer, such as clinical outcomes, genetic alteration, DNA methylation, or protein phosphorylation. Altogether, their results support the SHC1 role in tumor immunity and its possible prognostic and diagnostic value in multiple cancers. Also Xie et al. focused on cancer, taking advantage of the TCGA database. They used mRNA expression and clinical data of Pancreatic ductal adenocarcinoma (PDAC) patients from the TCGA database as training cohort to establish the prognostic model and from GEO database as test cohort to validate the model focusing on necroptosis-related genes (NRGs). They were thus able to construct a genetic risk prognostic model based on four NRGs, independent of other risk factors such as age and tumor grade and with a fair predictive diagnostic efficacy that might be used in PDAC patients.One of the greatest advantages of integrating multi-omics data is the possibility to exploit different datasets already available. Thanks to the improvement in technologies and bioinformatic tools, as well as the more and more affordable costs, omics data of many kinds have been generated at a remarkable hastening. Alongside, many databases that include clinical data together with genomic data, expression profiles, and other omics data are progressively becoming available, on both specific diseases and general populations. Just think of widely used databasessuch as TCGA  with already published copy number alterations data of different CRC samples, in order to identify a gene signature with a potential diagnostic and predictive value. The authors evaluated this gene signature expression levels in blood samples from CRC patients using data from GTEx portal, to test blood as a biological sample from which to extract information in a non-invasive way. Also Chen et al. focused on CRC datasets. Through the development of a new method, they identified gene networks significantly correlated with CRC survival that included genes with crucial roles in cancer progression and that could serve as potential prognostic biomarkers. With the same purpose, Needhamnsen et al. published a study on patients affected by Multiple Sclerosis (MS) in which they combined small RNA data and methylome data by applying the multiple co-inertia analysis (MCIA) multivariate method. This unsupervised approach was applied to multi-omics data derived from multiple compartments, cell-free cerebrospinal fluid (CSF), CSF, plasma and peripheral blood mononuclear cells from the same 42 individuals. Their integrative approach allowed to distinguish between the relapsing-remitting form and the secondary progressive stage of MS patients.Besides the articles described above, many studies based on multi-omics analysis are focused on the field of tumor and have the goal of identifying biomarkers with a diagnostic and prognostic effect. Chen et al., designed a framework, Correlation-based Local Approximation of Membership (CLAM), which combines multi-omics data to identify co-expressed gene modules without the need across different datasets to share the same genes or samples and exploiting known molecular interactions in module detection, thus overcoming some common limitations of current methods. They applied their method to expression data from several cancer and normal tissue dataset finding higher precision and biological relevance in modules retrieval and a higher number of gene ontology (GO) terms in enrichment analysis in comparison to other known methods.An additional advantage of omics data is that they can be re-analyzed as information is added and new methods of analysis evolve. It is therefore compelling the development and improvement of analysis methods, their standardization and guidelines on both analysis and data control steps based on interdisciplinary approaches. Several articles provide an overview on the multi-omics analysis  with parBesides the necessity of improving data integration methods and guidelines, there are still several limitations to the multi-omics approach. Among the most evident, is the lack of external validation of the results obtained by multi-omics analysis in different biological samples. In addition, some of the common issues in human diseases studies are even more crucial in multi-omics studies. The risk of sampling bias and the importance of detailed phenotypes and clinical classification remains of utmost importance, including the definition of covariates related to clinical outcomes . FinallyIn conclusion, multi-omics analysis can improve the knowledge of the complex underlying biology and identify prognostic and diagnostic biomarkers, as attempted in the studies published in the present Research Topic. This might eventually pave the way to better prevention and personalized medicine: multi-omics are still at their dawn, with some methods still lacking maturity, but these approaches are definitely promising and worthy of being increasingly applied to the study of human traits and diseases.