Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest, while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Relationships between tumor genotype and phenotype can be identified from these data through traditional correlation analyses and artificial intelligence (AI) models. However, the radiogenomics community lacks a unified software platform with which to conduct such analyses in a reproducible manner. To address this gap, we developed ImaGene, a web-based platform that takes tumor omics and imaging datasets as inputs, performs correlation analysis between them, and constructs AI models. ImaGene has several modifiable configuration parameters and produces a report displaying model diagnostics. To demonstrate the utility of ImaGene, we utilized data for invasive breast carcinoma (IBC) and head and neck squamous cell carcinoma (HNSCC) and identified potential associations between imaging features and nine genes  for IBC and eight genes  for HNSCC. ImaGene has the potential to become a standard platform for radiogenomic tumor analyses due to its ease of use, flexibility, and reproducibility, playing a central role in the establishment of an emerging radiogenomic knowledge base.www.ImaGene.pgxguide.org, https://github.com/skr1/Imagene.git.https://github.com/skr1/Imagene.git.Supplementary data are available at  Diagnostic imaging techniques are routinely used in clinics and laboratories for the identification of tumor severity and progression  tumor regions of interest (ROIs) that are believed to represent core sections of tumor on images. Measurements such as spatial, volumetric, textural and intensity-based are extracted from image-based ROIs and analyzed using several techniques in correlation analysis and artificial intelligence (AI) , a group recently conducted a locoregional study using MRI images of glioblastoma tissues by generating heat maps corresponding to both core and boundary regions of specific tumor ROIs, revealing substantial genetic heterogeneity  that explain the performance of models in predicting and/or classifying either omics or imaging data from either imaging or omics data, respectively  and decision trees (DT). It further conducts classification of predicted labels at various decision thresholds (dts) yielding AUC values at such thresholds. Through these outputs, ImaGene allows users to perform their radiogenomic experiments more intuitively and helps them establish unambiguous conclusions that can highlight data-driven directions for future research. ImaGene provides an integrative platform that can be used with data from both histological as well as diagnostic imaging to reveal tumor-related spatial and temporal details that cannot be established through histopathology alone.www.ImaGene.pgxguide.org’ (https://github.com/skr1/Imagene.git) and operate it on a Linux Operating System (OS) using a Command-Line Interface (CLI). Once downloaded, users can set various configuration parameters available on the platform and run their experiments  on the Amazon Web Service (AWS) servers of Queensland University of Technology (QUT) and is also available as an open-access website ‘ide.org’ . Users ceriments . ImaGeneeriments . The codFor analytical operations, ImaGene requires two input files, each in Comma-Separated Value (CSV) format—one containing imaging features (and their measurements) and another containing omics features for a set of tumor samples. The imaging features can be acquired from feature extraction software such as PyRadiomics, LIFEx or RaCaT by processing the tumor images using the respective segmentation labels  two-way univariate, (ii) univariate to multivariate and (iii) two-way multivariate. These features (P-adjust < 0.05) are then transferred to the ML module for further processing. Setting the correlation threshold to −1.0 disables correlation threshold-based filtering of imaging and omics features and transfers all features to the ML module.The data is then transferred to the correlation analysis module where either Pearson or Spearman test is conducted for imaging and omics features per user’s selection of the correlation method . The outn method  is used X) where, {Y) where {X). ImaGene also provides users with an option to specify grid parameters for their experiments based on the options available in the scikit-learn library, which can be achieved by setting the ‘grid search’ parameter to ‘True’   . Users clue = 2) . Based oy) in the dataset, and the ratio of RMSE ‘RMSE(y)’ and standard deviation of the originally observed values ‘STDEV(y)’ (RMSE:STDEV) represents the prediction error of the model for each label as a function of the standard error (standard deviation) in the distribution of the observed label values (yobserved). Furthermore, users can utilize the identity (1) below depicting the relationship between R2 and RMSE:STDEV ratio  and the total variance that exists in yobserved (i.e. SST).The test dataset is scored by calculating the negative mean square error (MSE) and the RMSE. RMSE is calculated for each label    and 3.yobserved at various dts  for each and every gene label (y). Thus, if a normalized expression value belongs to a range of  increasing in steps of 0.1 (i.e. yobserved) falling below 0.5 will be set to 0 and the ones rising above 0.5 will be set to 1 for every gene label (y). The ypredict however would be retained as they are, i.e. in continuous form. Such a binarization of yobserved allows ImaGene to calculate AUCs for each label (gene) at various measurement cutoffs (i.e. dts) ultimately aiding users deem the best dt (or range of dts) at which a model is able to classify a label (gene) as high or low measured (i.e. expressed) for the given spectrum of imaging features  into groups of their high and low measures , such measures are first binarized for R2 > 0.25 during the test phase such that their significance can be estimated. The results from these tests are text files labeled as ‘validation permuts’, so that users can calculate P-values. The P-values can be calculated as no. of times AUCpermut ≥ AUCtest and R2permut ≥ R2test for AUC and R2 (respectively) for each gene called with AUCtest > 0.9 and R2test > 0.25 during the test phase. These thresholds were kept high to eliminate the permutation runs (tests) on genes showing low R2test and AUCtest values and to focus on confidently predicted genes only. This also helps keep the runtime of ImaGene optimal. These two features contribute to ImaGene’s robustness and reliability. In addition, ImaGene reports feature importances results as text files depicting how each radiomic feature weighted in prediction of a gene. These weights are either in the form of feature importances called from a Decision Tree Regressor or are model coefficients in case of other regression models, both calculated using respective modules in scikit-learn library . The next section describes the imaging and omics features that exhibited significant correlations and that were used to train the model along with the parameters that were used for training and testing the model. The next section is the ‘model interpretation’ section which provides metrics such as MSE, RMSE and K-fold cross-validation scores of the model, and various plots such as a bar plot representing the RMSE:STDEV ratio for each label feature, a scatter plot representing true versus predicted values of labels having RMSE:STDEV ≤ 1, and a two-dimensional plot representing dt values on the x-axis and AUC values on the y-axis to indicate the performance of the model in classifying labels (genes) into categories (high or low expression) at different cutoff values  from 0 to 1. These metrices aid users determine the model’s reliability in predicting and/or classifying labels. Furthermore, the reports also include similar metrices and plots from permutation tests on gene labels in the ‘validation permuts’ sections therein.Once an experimental run is completed, users can use the job tracking page to download a detailed HTML report. This report represents the alterations made to the imaging and omics data in each module of ImaGene in the form of scores and plots which help users acquire an overview of the process and deduce meaningful outcomes from the features data (Supplementary Report BC 2). The first section of the report provides details regarding the type of input data (i.e. imaging and omics features files), the number of sample entries common to both files and the type of normalization method used for the input data. The second section provides hierarchically clustered heat maps depicting statistical correlations between the imaging and omics features. Features having a correlation coefficient greater than the default threshold of 0.5 and an false discovery rate (FDR)-adjusted To demonstrate the performance and capabilities of ImaGene, we conducted two case study analyses of tumor imaging features already published in TCIA and corresponding gene expression data from TCGA pertaining to two major types of cancer—invasive breast carcinoma (IBC) and head and neck squamous cell carcinoma (HNSCC)  and the K-fold cross-validation splitter (cv) was set to 2 for LR and DT models, and 3 for other model types.Once the initial screening of the downloaded dataset was complete, the imaging and gene-FPKM features were processed based upon user-defined ImaGene parameter settings. For example, the absolute Pearson-based correlation coefficient threshold was set to 0.7 for linear regression (LR) modeling downstream and 0.6 for other model types depending on number of features required for the satisfactory training of different types of AI models, the P-value, aka P-adjust <0.05) with the FPKM values of a total of 17 genes as depicted in the reports (Supplementary Report LR CorrF BC1), i.e. when the correlation coefficient threshold was set to 0.7. Second, when the correlation coefficient threshold was relaxed to 0.6, an additional radiomics feature (Washout rate (K4)) and 15 additional genes appeared on the list of features exhibiting significant correlations (Supplementary Report DT CorrF BC2). Using this threshold and the expanded feature set, the training of the DT, MTL and MTEN models was found to be enhanced but none identified significant genes (P < 0.05) posttest with the test dataset. LR identified one significant gene posttest-SLC22A31 (P < 0.05) with at least one gene predicted by each model type .At the end of the experimental run using the above mentioned parameters, a comprehensive radiogenomics report was obtained for each model type that reported LR, DT, Multi-Task LASSO (MTL) and Multi-Task Elastic Net (MTEN) models (Supplementary Reports BC 1-5) that yielded the best results for the test dataset. Each model was generated twice in a fully automated fashion, i.e. executing two scenarios: one with and another without a prior correlation-threshold-based filtering of features in a single run attempt, with the latter including an additional feature selection step  prior to training and testing of the models to ultimately improve gene predictions by them. Note that the correlation filtering in the first scenario occurred on a nonnormalized dataset. Normalization occurred for the train and test datasets independent of each other, i.e. post-train-test split, depending on the normalization method selected by the user . The radSLC22A31 . On contThe biological significances of the identified genes were ascertained using existing literature. Wilm’s tumor gene 1 (WT1) is oncogenic as well as a tumor suppressor in breast cancer. The high expression of WT1 regulates proliferation, migration and apoptosis of breast cancer cells by interacting with molecules or signaling pathways such as the Caspase family, EphA2, p53, HER2, TP-alpha and ER-alpha . A loss In summary, ImaGene facilitates the systematic and user-controlled imaging-based prediction and classification of gene expression in IBC, demonstrating the capability of this platform to identify significant associations between imaging and omics features using correlation analysis as well as AI models in an algorithmic manner. It also allows complete control over multiple operational parameters, ultimately producing a transparent radiogenomics report along with all supporting information outcomes necessary for user interpretation.The TCGA HNSCC cohort was accessed to download 540 radiomics features from CT scans of 106 HNSCC patients from a previous imaging study conducted at Stanford University , and the K-fold cross-validation splitter (cv) was set to 3.The radiomics and gene-FPKM features from 106 samples were used for the experimental run along with the following parameter settings: (i) the absolute Pearson-based correlation coefficient threshold was set to 0.7 for LR modeling and 0.6 for other model types and (ii) the P < 0.05) genes posttest unlike LR that did yield a significant (P < 0.05) gene: MAL posttest (P < 0.05) with at least one gene predicted by three model types , none predicted by LR . These reports presented significant correlations between 12 radiomics features  and FPKM values for 21 genes (Supplementary Report LR CorrF HNSCC1). When the correlation coefficient threshold was relaxed to 0.6, 29 additional radiomics features in the wavelet category and 33 additional genes appeared on the list of significant correlations. This expanded feature set was found to improve the training of the DT model (Supplementary Report DT CorrF HNSCC2), although it did not yield any significant  was involved in the resistance against anti-cancer drugs and invasion of lung adenocarcinoma cell lines , and as Claudin-16 (CLDN16) is a part of the seven-gene signature that predicts prognosis in OSCC  which coOverall, these two case studies highlight many important genes that may contribute to IBC and HNSCC incidence and/or progression, thus illustrating the capability of ImaGene as a tool for identifying meaningful associations between imaging features and gene expression data in tumor patients using correlation analysis or feature selections. When used in conjunction with AI modeling techniques, ImaGene is a meaningful resource that can enable valuable tumor radiogenomic evaluation and reporting.ImaGene is a web-based platform that offers a comprehensive radiogenomic framework to perform statistical correlations and AI modeling techniques as a means of linking molecular and tumor imaging data. It enables the rapid screening of significantly correlated radiographic and omics features for particular tumors, as well as the construction and testing of different types of AI models using these features as a means of aiding in the prediction of biologically relevant omics features based upon imaging findings. This platform provides control over radiogenomic experiments by hosting multiple configuration parameters that can be defined by end users or left at default values. It also obeys FAIR principles . Relative to other radiogenomic applications such as Imaging-Amaretto, Imaging-Community Amaretto and Musa, ImaGene’s features have the potential to make it widely adoptable in clinical settings and laboratories worldwide as a tool for testing associations between imaging and omics data for all tumor types, paving the way for significant discoveries in the fields of radiogenomics and oncological research.As a proof of concept, we tested the performance of ImaGene for the prediction and classification of omics profiles from imaging features using the IBC and HNSCC datasets. We identified strong associations between the expression of genes WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2 and SLC22A31, and their imaging features such as tumor size, shape, enhancement and kinetic curve assessments in the IBC dataset (Supplementary Report LR CorrF BC1, Supplementary Report DT CorrF BC2, Supplementary Report LR FS BC3, Supplementary Report MTL FS BC5, Supplementary Report MTEN FS BC6, and ImaGene can facilitate collaborations between researchers, making the sharing of radiogenomics reports more convenient and ultimately leading to the advancement of research in the cancer and precision medicine space. Our case studies have sought to provide a basis for the radiogenomic analysis through the processing of imaging and omics feature data in two tumor types while permitting robust control over experimental parameters. We have successfully demonstrated the configuration and usage of statistical correlations and AI modeling on our platform, leading to the construction of reliable AI models that can predict biologically relevant omic features from imaging data. Analysis of multiple datasets of different types and subtypes of tumors in ImaGene can contribute to the development of a radiogenomic knowledge base that can be used in cancer research, drug discovery and precision medicine, allowing the outcomes of this platform to undergo translation for clinical use in the near future.


Interesting, many of the age clock genes identified include known targets mechanistically linked to AMD and aging, such as GDF11, C16ORF72, and FBN2. This study provides new observations for retina age clocks and suggests new applications for monitoring in vitro neuronal differentiation. These clocks could provide useful markers for AMD monitoring and possible intervention, as well as potential targets for in vitro screens.Age-Related Macular Degeneration (AMD) is a bilateral ocular condition resulting in irreversible vision impairment caused by the progressive loss of photoreceptors in the macula, a region at the center of the retina. The progressive loss of photoreceptor is a key feature of dry AMD but not always wet AMD, though both forms of AMD can lead to loss of vision. Regression-based biological age clocks are one of the most promising biomarkers of aging but have not yet been used in AMD. Here we conducted analyses to identify regression-based biological age clocks for the retina and explored their use in AMD using transcriptomic data consisting of a total of 453 retina samples including 105 Minnesota Grading System (MGS) level 1 samples, 175 MGS level 2, 112 MGS level 3 and 61 MGS level 4 samples, as well as 167 fibroblast samples. The clocks yielded good separation among AMD samples with increasing severity score viz., MGS1-4, regardless of whether clocks were trained in retina tissue, dermal fibroblasts, or in combined datasets. Clock application to cultured fibroblasts, embryonic stem cells, and induced Pluripotent Stem Cells (iPSCs) were consistent with age reprograming in iPSCs. Moreover, clock application to  Age-related macular degeneration (AMD) is one of the leading causes of central vision loss among individuals aged 50 years and older –9. As thIt is well accepted that AMD is complex multi-factorial disease , 16–19. Epigenetic biomarkers of aging include epigenetic clocks that are useful to estimate the biological age and that are built through measurements of DNA methylation, microRNAs, mRNAs as well as other epigenomic features. Epigenetic clocks are relevant for determining environmental and genetic factors that impact the aging process, for providing potential markers for disease monitoring, and to accelerate analyses of potential rejuvenating treatments. Research to identify biomarkers and predict biological age used data that ranged from DNA methylation to transcriptomics, microbiome, proteomics, frailty assessment, and neuro-imaging , 29, 30.in vitro neuronal differentiation has not been fully addressed. Hunter et al. found hypermethylation of Glutathione S-transferase isoform mu1 (GSTM1) and mu5 (GSTM5) promoter in RPE cells from AMD donor eyes when compared to control , regardless of whether clocks had been trained in retina samples, dermal fibroblast samples, or in combined retina-dermal datasets. In addition, clock application to other related cells  confirmed age reprograming in IPSCs. Interestingly, model application to We first utilized human retina gene expression human data (NCBI Gene Omnibus ID: GSE115828) collected with Illumina HiSeq 2500 , 46, 47.eps) and (ii) minimum points (MinPts). The eps that was the radius of the neighborhood around any point, was termed as epsilon-neighborhood (e-neighborhood) of that point, while MinPts was the minimum number of neighbors inside eps. Whenever a point had a neighbor count score that was higher than or equal to MinPts, the point is stated as a core point. If the number of the neighbors of any point was less than MinPts, but the point belonged to the e-neighborhood of a core point, the point was stated as border point. When a point was neither a core point nor a border point, that point was treated as a noisy/outlier point. Our goal was here to determine the dense regions which could be estimated through the number of points/objects close to a specific point. Here initially we determined the knee-point by K-nearest neighbor (KNN) distance plot. KNN distances were calculated and sorted to estimate the knee-point. After that, these were scaled to range between 0 and 1, and the derivative was evaluated. The first point in which the derivative was higher than a certain value , was treated as the knee-point. The scaled distance score of the knee-point was termed eps-neighborhood value.We applied a well-known noise removal cluster algorithm, “Density Based Clustering of Applications with Reducing Noise” (DBSCAN) , 51 usinh” as 5,000 in the plot to determine the knee point. That knee was used as eps score in the next step i.e., DBSCAN clustering to identify the outlier features. Using DBSCAN clustering, we obtained 75 noisy features (outliers) and omitted them from the further analysis. We then utilized the remaining noise-free features  for clock model development. For the full feature set of dermal fibroblast data, using DBSCAN clustering, we identified 58 noisy features and discarded them from the further analysis. We then utilized the remaining noise-free features  for clock model development.For the case of retina data with full feature set, we applied kNN distance plot with the data while k was set as the sample size plus 1 (= 453 samples +1). Here we fixed height “After outlier detection through DBSCAN, data sets were processed for further analysis. We utilized leave one out cross-validation (LOOCV) in specific datasets  to divide a dataset into training and test sets. In LOOCV, only one sample would be used as test set, while remaining samples belonged to the training set. That step would be repeated for each of the remaining samples. We chose the subset of the expression data containing only those training samples, and the age of those training samples from clinical data. On the other hand, we also selected the subset of the expression data consisting of only those test samples, and the age of those test samples from clinical data.After identification of training and test samples through LOOCV, we applied “glmnet” regression R tool –54 on thij was termed as the gene expression profile at i-th sample and j-th feature, whereas yi was the logarithm converted chronological age of i-th sample. The combination of lasso and ridge regulation methods with each having equal preference had been utilized here for the inclusion an additional penalty  to the coefficients of predicted variables. Here λ (> 0) was basically a tuning parameter that helps to regulate the whole penalty against the respective coefficients, whereas α was another tuning parameter whose ranges lied in between 0 and 1 (0 < α < 1) revealing the compromise between ridge technique (α = 0) and lasso methodology (α = 1). We fixed α to 0.5. λ was chosen through LOOCV  internally by following the one-standard-error policy. After determining the specific λ, we selected features and obtained their coefficients depending upon the estimated λ. Thereafter, we discarded those features whose coefficient was zero. And then we continued with remaining non-zero features for age prediction.where m referred to as the number of samples and n denoted the number of features. Here xFor the full feature set of retina data, we used MGS level 1 samples (control samples) to build age clock models. We applied LOOCV for splitting the data into training set and test set. In LOOCV, only one sample would be utilized as test set and remaining 104 samples belonged to the training set. That step was repeated for each of the remaining samples. For training, we selected the subset of the data consisting of only those training samples, and the age of those training samples from clinical data. Similarly, for the test model, we picked up the subset of the transcriptomic data having only those test samples, and the age of those test samples from the clinical data. After determination of training and test samples with LOOCV, we used “glmnet” regression on the training methylation data having 104 samples and 17,978 features as response variable and logarithm transformed training aging data having chronological ages of 104 samples as predicted variable, and then determined a lambda (λ) score for which classification error was equal to the least square error (lse). The corresponding regression model computed coefficients for all features among which non-zero coefficients  will be identified. These corresponding features having non-zero coefficients were termed as selected features for each model. In addition, we computed the occurrence of each gene in all those evolved integrated models. Similarly, for the restricted feature set of retina data, we followed the same pipeline  while working on training data containing 104 MGS1 samples and 5,321 features, and test data consisting of only one MGS1 sample and 5,321 features during the iteration of “glmnet” regression.For the restricted feature set of dermal fibroblast data, using the 143 samples, we utilized LOOCV first and then applied glmnet regression on the training expression data containing 142 samples and 5,321 features as response variable and logarithm transformed training aging data having chronological ages of 143 samples as predicted variable, and then determined a λ-score for which classification error was equal to the least square error. The regression model computed coefficients for all genes of which non-zero coefficients  will be identified. Those genes having the non-zero coefficients were behaved as the selected features for each evolved model.For the restricted feature set of joint data, using the 248 samples, we utilized LOOCV to split the entire data into training set and test set. In LOOCV, only one sample was treated as test set while remaining 247 samples was used as the training set. We repeated that step for each remaining sample . After LOOCV, we applied “glmnet” regression methodology on the training methylation data containing 247 samples and 5,321 features as response variable and logarithm transformed training aging data having chronological ages of 247 samples as predicted variable, and then determined a λ-score for which classification error was equal to the least square error (lse). The regression model computed coefficients for all genes of which non-zero coefficients  will be identified. Genes having non-zero coefficients were selected as features in each model.MAE = med, where med was median score, ŷi referred to the predicted score of the i-th sample and yi denoted the respective true  score. MAE was a minimization objective . Spearman’s correlation is well-known linear similarity finding method that is useful here to determine the similarity between predicted age vectors and original  age vectors, while gene occurrence (frequency) is termed as the number of times it appears among all regression clock generation. Here occurrence (in %) = occurrence*100/total number of clocks. In addition, rank wise cumulative sum of occurrence for each gene was computed. Here, rank-wise cumulative sum of occurrence (in %) = rank wise cumulative sum of occurrence * 100/total number of events, where “total number of events” denotes the summation of occurrence of all initially participating genes during clock generation. Finally, we compared the different regression models through the intersection of non-zero occurrence genes. We used Venn diagrams to represent this intersection. After generating the evolved age clocks from each kind of data, we applied on other data samples to predict them using the model coefficients. Here we mainly utilized four evaluation metrics: (i) age acceleration (AA), (ii) Median absolute error (MAE), (iii) Spearman’s correlation , and iv, 57 is d+) and 56 individuals with negative age acceleration (AA–). This ∼50% positive/negative age acceleration fits the expectation for a training set. The maximum value of AA was +20.4 years while the minimum value of AA was –19.1 years. The model for which we obtained the maximum AA had 94 features/genes, while the model for which we identified the minimum AA had 54 features/genes. The model yielding the lowest absolute AE of 0.003 consisted of 78 features/genes.We initiated our analysis with transcriptome data consisting of 18,053 features/genes and 453 Retina samples . The set includes control samples (MGS1) and three levels of AMD severity . After quality control and noise removal , we carried forward 17,978 features, and applied leave one out cross-validation (LOOCV) to build 104 age clock models using MGS 1 samples . For each model, we computed the absolute error (AE) and age acceleration (AA) for the test sample of the LOOCV. For MGS 1 samples, we identified 49 individuals with positive age acceleration (AA+), and 54 samples with negative age acceleration (AA–). The maximum value of AA was 18.5 while minimum value of AA was –17.4. The model for which we obtained the maximum AA used 64 features, while the model for which we identified the minimum AA used 98 features. The model yielding the lowest AE (0.07) used 80 features. We conclude that the smaller set of evolutionarily conserved genes yielded age clock models that were comparable to those built with the larger gene set.We built age clock models using a restricted set of 5,321 features comprising genes that are (i) evolutionarily conserved between human and mouse, and (ii) shared across all datasets used in this study. Using this conserved and conservative feature set, we identified 51 samples with positive age acceleration  was observed for most of the models (= 206 models), while positive median age acceleration (MAA+) was only observed for 4 models. Minimum value of model wise MAA was found as –1.72, while maximum value of model wise MAA was 0.06. Minimum value of model wise MAE was 0.91, while maximum value of model wise MAE was 7.61. For AMD MGS 2 samples, negative median age acceleration (MAA) was observed for all models. Minimum value of model wise MAA was found as –4.39 years, while maximum value of model wise MAA was –1.39 years. The minimum and maximum values of model wise MAE was 5.12 and 7.36 years, respectively. For AMD MGS 3 samples, MAA ranged from –11.66 to –6.80 years; MAE ranged from 7.30 to 12.12 years. For AMD MGS 4 samples, MAA ranged from –13.26 to –7.23 years; MAE ranged from 8.53 to 13.26 years .Next, we applied all 210 retinal age clocks (105 clocks developed with the full gene set and 105 clocks developed with the conserved gene set) to the MGS1-4 samples, and computed model wise Median Absolute Error (MAE) and model wise Median of Age Acceleration (MAA). For MGS 1, negative median age acceleration . In the testing phase we built 143 models with LOOCV. The analyses yielded 69 samples with positive median age acceleration (MAA+) and 74 samples with negative median of age acceleration (MAA–). We estimated MAE as 3.49, considering all 143 dermal fibroblast models together. For the prediction of all 143 dermal fibroblast samples themselves, the model-wise MAE values ranged , thus the minimum MAE value (= 2.24) was extremely good. The model-wise Spearman’s rank correlation (rho) between the vector of predicted age and the vector of chronological age of dermal fibroblast samples for each model ranged from 0.89 to 0.99.MLLT11, CNKSR3, FAHD2B, FBN2, ALKBH3, FAS, SLC22A15, RPN2, BST2, XPNPEP1, CCDC8.Finally, we computed the frequency (occurrence) of each gene (feature) in all 143 dermal fibroblast models . The totRHOJ, VTI1A, CDCA7, LYPD1, PRSS12, MEST, LGR5, TMEM132B, PCDHB2, ARSK, FAT1, BMP4.We used the dermal fibroblast clocks to predict age in the retina of MGS1-4 samples . All 143Here we pursued integrated models using both retina and dermal fibroblast samples for training . We normalized the evolutionarily conserved datasets gene wise using min-max normalization and produced a combined set of 248 samples that included both the 143 dermal fibroblast samples and 105 MGS1 samples. In the testing phase we built 248 models with LOOCV. We observed 88 samples with positive age acceleration (AA+) and 160 samples with negative age acceleration (AA-). The MAE value considering all 248 integrated models together was 22.02 years. In addition, we evaluated model performance in the retina and dermal samples separately. For the prediction of all 143 dermal fibroblast samples themselves, the model-wise predicted MAE values ranged . Spearman’s rank correlation between chronological age of dermal fibroblast samples and predicted age were generally high (range: 0.77–0.81). Thus, for the dermal fibroblast samples, the integrated model fits reasonably well. For the prediction of all 105 MGS1 samples, the model-wise predicted MAE values range (22.68–25.64) was nearly twice as large as the MAE for skin samples. MAEs were larger for MGS4 samples (range: 42.12–48.59). Noteworthy, joint dermal-retina models replicated the same pattern we observed for retina-only and skin-only models, and readily separated retina samples in accordance with MGS pathological score .SYNE2, LNX1, VDR, PLCG1, BCAS4, RPAIN, GDF11, FBN2, CPNE8, ALKBH3, MYO1D, ZNF518B, TEAD4, GPC2. We provided the occurrence plot of the top 50 ranked genes in the rank wise manner in In addition, we estimated the occurrence of each gene in all 248 integrated models. Here total number of events for 248 models was 5,886. Out of all 5,321 input genes, 39 genes were represented in at least one clock . Top 38 most frequent genes were accounted for 99.98% of all events . Top 24 most frequent genes were selected over 50% occurrence in all models  and accounted for 97.76% of all events. Interestingly, the top 14 genes that had 100% occurrence, were MLLT11, FBN2, ALKBH3, FAS, BST2, XPNPEP1, GPC2, ZNF518B, TEAD2, SYNE2, DUSP2, CPNE8, GDF11, C16orf72, CKAP4, TEAD4, PEG10, BCAS4, APBB1IP, SEMA4C, RIN2, LNX1, RNF149, EXOC8, RPAIN, and 2 genes shared between retina MGS1 and joint retina-dermal clocks, viz., LAMA4 and MYO1D. Interestingly, literature evidence has implicated some of these genes in AMD   –61.To verify model behavior, we assessed the ability of our clock models to predict age in cell culture fibroblasts, reprogrammed primary fibroblasts, and embryonic stem cell lines. We used a dataset comprised of two neonatal samples  and their respective iPSC , two samples from a 50-year-old individual  and their respective iPSC , and two embryonic stem cell lines (H1 and H9). In these samples, we computed the average predicted age across all models of a given type . Reassuringly, all models displayed similar overall behavior across samples , with siin vitro neuronal differentiation. To address the issue, we used a dataset consisting of a time course of cellular differentiation from neuronal stem cells to neurons. While we did not expect models trained on chronological age in years to be directly applicable to neuronal differentiation in days, a correlation between predicted age and differentiation time might be expected. Indeed, the retina models did not numerically recapitulate differentiation day; however, we noted a relative increase in “age” from day 19 to day 77 that was reproducible in two replicates   . All in in vitro neuronal differentiation suggests new opportunities for broader applications.AMD is a bilateral ocular condition especially noticed in older individuals resulting in irreversible vision impairment caused by the progressive loss of photoreceptors in the macula, a region at the center of the retina. In general, AMD risk is highly dependent upon factors such as a patient’s advanced age, familial history of AMD, signs and symptoms of pigmentary and drusen aberration, as well as lifestyle  –64. TreaC16ORF72, GDF11, FBN2) had been shown to play a role in aging, AMD, or other age-related diseases. For instance, Ratnapriya et al. (FBN2 (Fibrillin 2) gene’s impact in AMD . It is well-known that telomere erosion in cells with insufficient amounts of telomerase reverse transcriptase (denoted as TERT) leads to age-associated tissue dysfunction as well as senescence at least partially through p53 , a secreted factor in the TGFß family of cytokines , while other factors  were not incorporated at this stage. In future, we envision extending this work through the inclusion of new feature selection strategies along with other machine learning approaches toward ever more useful biomarkers of aging and AMD. Furthermore, implementation and application of AMD targeted age clocks in screens for therapeutic intervention as well as clinical and population settings might be promising. The efforts could yield useful new therapeutics as well as improved tools for monitoring AMD risk and disease progression.Retina (AMD) gene expression human data has been collected from NCBI Gene Omnibus ID: GSE115828, while dermal fibroblast gene expression data, primary fibroblast dataset and neuronal differentiation dataset can be retrieved with NCBI Gene Omnibus ID: GSE113957, GSE97265, and GSE56796, respectively.Ethical review and approval was not required for the study in accordance with the local legislation and institutional requirements.SM and BL conceived and designed the study, analyzed the data, and wrote the manuscript. SM collected the data and wrote the programming code. FG, DB, and DV contributed to reagents and expertise. All authors edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease.On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development.This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system. Healthcare centres urgently need novel technologies to identify symptoms for accurate diagnosis and severity prediction as a critical area. Coronaviruses, particularly those of the genus beta coronavirus , are highly pathogenic agents of respiratory disease whose highly variable genetic diversity and diverse host-adaptive features make them lethal and devastating worldwide . This diMost current research on COVID-19 has focused on its epidemiological and clinical characteristics , 5. TherSeveral bio-computing solutions, including machine learning (ML), are currently being developed for the diagnosis and severity prediction of COVID-19. Its’ high precision, high automation, and interpretability can benefit the public health system to mitigate COVID-19 effects, having great potential to interpret high-dimensional and complex datasets. Recently, several studies has explored different ML models for COVID-19 diagnosis and prognosis –8. BrinaWhile, there are several models and datasets for COVID-19 detection, there is a handful of dataset and models for predicting the severity of COVID-19. Predicting severity is an important task to identify the progression of the disease and direct severe patients to immediate medical support—which will ultimately reduce the mortality of the patients. Several biological data, such as peripheral blood samples and blood oxygen index, have been used as essential indecs to predict the severity of COVID-19 patients. Recent evidence suggests that the severity of the disease depends largely on host factors. Many studies about the pattern of death with infection reveal surprising results: the cause of death cannot usually be attributed to the pathogen or the direct effects of any associated toxins it produces. Instead, it results from a systemic inflammatory response by the host’s immune system; in a way, our immune system destroys our health . These sInitial studies were based on single-omics data; for example, Macías-García et al.  proposedAt present, the research on covid-19 using ML mainly focuses on the clinical information of covid-19 patients. Often the host-response, e.g. early stage of COVID-19, at the physiological level is undetectable, which can be captured easily at the molecular level. The host response at the molecular level is vital and often provides numerous features for ML models to train on. Besides, to make the scenerio even more complicated, the responses often modulated several bio-molecules and underlying pathways—which can not be answered by analyzing a single omics study. There is a scarcity of ML models for omics-based dataset to detect covid, and extend the modeling towards developing severity prognosis modelling.The aim of our research is to apply the value of a large number of omics data to develop high-precision diagnostic and prediction tools for covid-19. We built a covid-19 diagnosis and severity models, based on single and multi-omic data. The study showed potential to be applied in diverse omics datasets, related to different respiratory diseases.First we explored the single omics dataset for diagnosis and severity prediction model. We collected DNA-methylation , RNA-seqWe used several single omic data to build ML models for COVID-19 negatives and positives classification. We first used DNA methylation data and RNA-seq data. We used 5-fold cross-validation on a total of 128 and 54 available samples in the DNA methylation and RNA-seq datasets, respectively, so the total number of samples tested in each fold was around 25.6 and 10.8 in these two datasets.Table Since the data has high dimensionality, we applied PCA and trained the model under the same conditions. With a dimensionality of 100 after dimensionality reduction, the performance of the model improved. In the case of MLP, the dimensionality reduction resulted in a significant performance improvement of around 0.37 in RNA-seq data, as too high dimensionality can make MLP harder to fit or overfit due to the complexity of the network structure.In addition to the performance improvement, the feature transformation significantly reduces time and machine costs when training the model. In the same environment, the training time and RAM usage before PCA processing is about three to five times that of the processed data. Therefore, an appropriate feature transformation approach can significantly improve the performance of the model and reduce the learning cost.First, we predicted COVID-19 severity by DNA methylation data, proteomics data, and metabonomics data. In the processing of high-dimensional data, we analyzed and confirmed that PCA has a better optimization effect on the training of ML models on high-dimensional data. Therefore, we used PCA to reduce the dimension of single omics data used for COVID-19 severity classification, and the dimension selection was based on the number of training set samples.On different data sets, there are different criteria for distinguishing the severity of COVID-19, and it is divided into different numbers of severity levels. In DNA methylation data, we have three different severity levels, and in proteomics data, there are two severity levels. Therefore, the confusion matrix cannot be used to directly compare the classification results between different omics data. So, we evaluated the performance of classifiers on different omics data through different evaluation indicators Table .Table 2TOur model got 0.70 accuracy for COVID-19 severity classification on different data sets. In DNA methylation data, the LR model achieved 0.76 accuracy. We have achieved a preliminary prediction of the severity of COVID-19 on the single group data. There was no significant improvement in the performance with various optimisation methods and parameter adjustments. The probable reason for such a low score is the limited features within a single omics data, while the prediction of COVID-19 severity involves complex molecular interaction among diverse molecules. Besides exploring other feature extraction methods and hyper-parameter optimizations, there is an enormous potential of improvement by integrating multi-omics data. Therefore, it directs us to mine multi-omics data for a more complete prediction scheme.In this experiment, we used a multi-omics dataset, containing transcriptomics, protemics, metabolomics and lipidomics . The datIn order to classify and predict the severity of COVID-19 cases using multi-omics data, we used 100 samples obtained containing their 4-omics data. Each sample had transcriptomic, lipidomic, proteomic, and metabolomic data. For these 100 samples, we first preprocessed (discussed earlier) and obtained 13,263 features for transcriptomics, 647 features for lipidomics, 517 features for proteomics, and 105 features for metabolomics.We used an autoencoder model, taking the stacked matrix of the 4-omics data as the input and output of the model, and then extracted the 500 nodes of the bottleneck layer as the result of the feature transformation. As the performance of the autoencoder is strongly influenced by the environment, the hardware environment, including RAM and GPU resources, we, conducted three iterations of the model to ensure the stability of the experimental results.For each transformed feature, we performed a one-way correlation analysis. As the features generated by the autoencoder were not fixed, by analysing the Kendall correlation between the features and HFD-45 , we retained an average of 455 features after three iterations and penalised them by elastic network regression to obtain a final sample of 42 features for clustering.After clustering, we got two obvious subgroup by their distribution. We name the subgroups by G1 and G2. By looking at the clinical data of the two subgroups, we analysed the severity differences between the two subgroups obtained by clustering. We reduced the dimension of the sample into two dimensions by PCA. The difference in severity between the two subgroups was clear, and it was found that the severity of G1 was higher than that of G2 because the HFD-45 value of G1 was much lower than that of G2. To assess the validity of the clustering results, we compared each pair of sample combinations between the two subgroups. The predicted and actual results are consistent if the HFD-45 value of the sample from the subgroup with less severity is greater than the other comparison sample.By comparing the values of HFD-45 between the two subgroups, we observed that the subgroup labels generated by clustering obtained a good agreement with the c-index of 0.76. At the same time, several indicators showed significant differences between these two subgroups, with a mean HFD-45 of 15.87 and 27.38 for first and second group. The considerable variability between the two means indicates a good differentiation in COVID-19 severity between the two subgroups. Therefore, the generated clustering labels effectively reflect the severity of COVID-19, with good discrimination between the different clinical stages of the case sample.A good severity classification cannot be achieved by using a single indicator as a classification label, so we cluster the samples and then performed supervised learning. We used the feature-processed sample data as training data, and the clustered 0–1 labels and HFD-45 median values as learning labels for the supervised models. We constructed SVM, RF, LR, KNN, RUS, and MLP models, obtained average results using 5-fold cross-validation, and repeated the experiments 3 times to ensure stability. The AUC, accuracy, F1 score, and c-index are shown in Table We have observed that the labelled subgroup generated using the clustering training performed superior on several models. The RF and RUS models achieved significantly good performance here, as the labels were clustered by K-means, whose decision process is to select close category labels based on sample characteristics, similar to the decision process of ML models such as RF and KNN, and therefore more likely to achieve good performance. However, MLP was unable to fit the data better and therefore does not perform as well as the classical ML models.While using cluster labels, we obtained a high prediction accuracy using the RF model. When training the model, we have achieved the correct classification for each data point through 5-fold cross-validation. The reason for high accuracy is that the sample size of test data after k-folding is small, and the model can predict all samples successfully. Secondly, the labels were obtained from individual clustering subgroups. Clustering labels affect the decision-making process of RF with high accuracy, which also indicates that, there is no overfitting problem within the prediction. However, we only obtained accurate predictions based on the subgroup labels, and a c-index of 0.76 reflects the prediction effect of the model on the sample ground truth. While using HFD-45 median values, we selected 100 samples with an overall median HFD-45 of 26 and a mean of 22. Since the presence of deceased cases, as well as undischarged cases with an HFD-45 value of 0, would have an impact on the overall distribution, we chose a median HFD-45 = 26 as the classification criterion and labelled patients as severe and less severe.From Table The use of different combinations of omics data as input features had a significant impact on the performance of the autoencoder. Figure The autoencoder model using 4-omics achieves the highest c-index, reaching 0.72. This result indicates that the multi-omics data is more suitable for model construction than the single-omic data. The c-index of most combinations with transcriptomics is high, and a c-index higher than 0.6 indicates that the severity correlation between Transcriptomics and COVID-19 is stronger. The prediction performance with single Metabolomics showed the least value of 0.49. Within single omics dataset, the c-index of Proteomics and Transcriptomics dataset are similar (0.66). Interestingly, the c-index of the combination of Proteomics and Lipidomics reached 0.68, but the addition of Metabolomics reduced it to 0.62. The results showed that the prognostic ability of each omics data may not be additive.To demonstrate the superiority of our Autoencoder-based deep learning model, we have conducted a comparative analysis of related models with our model. In addition to autoencoder-based multi-omics feature integration, traditional dimensionality reduction methods, such as principal component analysis (PCA) and cluster reduction methods, such as integral cluster analysis (iCluster) were also incorporated to evaluate the performance of multi-omics integration methods.p values In the first method, we use PCA for feature transformation. Since our sample size was only 100 cases, we restricted PCA to reduce the feature dimension only to 100. After PCA converted the initial features to 100 principal components, Kendall correlation analysis was performed on the selected new features and only features with In the second method, we used iCluster for dimensionality reduction. Unlike PCA and autoencoder, iCluster analysis does not have to transform the initial omics features into new features, but rather groups the samples based on four omic data. Samples were clustered into groups directly based on the features from multi-omics data. iClusterPlus showed good predictive efficacy in both survival subgroups with a c-index = 0.62, but still, no significant improvement over the autoencoder-based model. The results of the comparison by c-index suggested that autoencoder-based multi-omics integration is superior to these alternative methods.To demonstrate the robustness of classification in predicting prognosis, we built two supervised classification models based on proteomics and metabolomics. We predicted the classification labels of samples from the validation dataset. We first performed Min-Max normalisation on each of the omic data. Then we selected the top N features of the most relevant to the clustering labels (obtained from the K-means) based on an analysis of variance (ANOVA). We set the default N value to 50 for proteomics and metabolomics, and 100 for transcriptomics and lipidomics. The top 20 significant features for each group are shown in figures: lipidomics and transcriptomics in Fig. The comparision of the top 50 important features within our model (proteomics and metabolomics) and another COVID-19 severity prediction study, Shen et al.  have shoBesides, in the supervised learning models, independent omics data obtained results similar to training data. Proteomic data obtained the best performance on the RF model, with a c-index of 0.72. Metabonomics data obtained a c-index of 0.68 on the RUS model. The validation for robustness exemplifies the value of the information we obtained about the omics features for application on additional new dataset. The method we use not only yields good results on the training dataset, but also with the trained model, which can be used with the same type of omics data.In order to demonstrate the originality and validity of our work and its contribution to related work, we compare our results with those achieved by current related work in two ways: (1) omics data in any disease, and (2) omics and other data in COVID-19 disease only. We further discuss the advantages and shortcomings of each study, including our work.Past studies have used a combination of ML models and omics data to achieve an accurate prognosis for different diseases. We compared our achievement with several representative works in different evaluation methods, shown in Table Compared with the current extensive schemes, the performance of our methodology is significantly higher. This result may be due to the severity of the influence on the omic components of patients with COVID-19. Generally, severe cancer patients will have extensive and complex physiological changes affected by the disease. It results in complex omics data, which is not easy to predict the prognosis of the disease. We compared our study with the prediction of Alzheimer’s disease by Park et al.  based onOur current research are consistent with previous studies on liver adenoma  and neurAt present, many studies have explored the diagnosis and prognosis of COVID-19 from multiple dimensions and directions. We have compared our research with some of these studies Table .Table 5CWe have observed that, compared with the ML prediction model based on CT images, cough signals and clinical texts, our model achieved better performance. It might be due to the wide features extracted from molecular dataset. Omics dataset are often able to detect the onset of diseases, which cannot be visible in physiological responses or behaviours. Previous studies , 7 used Overmyer et al.  proposedWe obtained better results for both the tasks of COVID-19 diagnosis and severity prediction. We implemented effective feature processing methods to solve the problem of high dimension and low sample size, which improves the performance of the model. To summarise the contribution of our work—we have first evaluated different single omics datasets with varying feature extraction methods and strategically progressed towards developing deep learning models for multi-omics dataset, related to COVID-19. Our study proved that, a combination of multi-omics dataset gave better predictive scores, compared to any score generated from any single omics data. We compared our models in terms of other diseases and also related to COVID-19 models, and found that our model outperformed all other disgnosis and severity prediction models. We give an effective COVID-19 diagnosis and prognosis method based on ML, which can be used in the healthcare system for infectious disease control and graded treatment, to some extent improving public health governance.This research has generated several potential and promising directions for future work. First, we will investigate the ability to generalise the findings. Our current work can only guarantee validity in the four-omics features for limited number of samples. To contribute more to public health, we will further investigate other large-volume dataset, as well as increase the diversity of our dataset by selecting samples from different backgrounds. It will also be possible to enrich the type of omics data we use and obtain more extensive conclusions. We will use other ML models with complex structures for learning. Further, our current work is limited to the use of omics data for diagnosis, with a lack of experimentation with other medical data. In future work, we will add features like clinical information and patient health status to the input of the model. We analysed the Pearson correlation of clinical information and cluster label, where we observed that the label showed a strong correlation with multiple clinical information, such as Lymphocytes per cent. We would explore the optimisation and impact of additional data on current protocols. To investigate the use of the kind of combination that would allow for better diagnosis and prognosis of COVID-19 patients. Future research shall also focus on using pre-regulated and down-regulated expression genes from the omic signature for ingenuity pathway analysis. This approach would help investigate the value of our results for applications in biological and disease research.This research first diagnosed COVID-19 (negative and positive classification), followed by severity prediction. We collected and processed multiple single omics data: DNA methylation, RNA-seq, Proteomics, and Metabolomics. Details on these datasets are included in Additional file Removing abnormal and erroneous data was the first step in data preprocessing. Five samples were removed from the proteomics and metabolomics dataset as they did not have proteomic data. For the treatment of null values in the dataset, we removed the feature with null values greater than 20% and performed zero value padding. As the data dimension was much larger than the number of samples, we reduced the dimension by principle component analysis (PCA) to efficiently train the models. For the samples, we reduced the dimension of features to the number of samples. We report the models’ performance, both with and without PCA, to demonstrate the role of PCA in the dataset. In our datasets, COVID-19 negative samples only account for a small part, resulting in severe imbalance data. To tackle data-imbalance problem random undersampling boost (RUS) was optimised using AdaBoost method. To compensate the imbalance and ML performance, we further applied SMOTE (synthetic minority oversampling technique) sampling method for data balance , 27.We implemented several ML models, namely support vector machine (SVM), random forest (RF), logistic regression (LR), K-nearest neighbourhood (KNN), random undersampling boost (RUS), and multilayer perceptron (MLP) algorithms. We adjusted the parameters of the MLP, including the number of connected layers and the number of nodes, to get better results for different dataset. The number of layers, nodes, dropout, and activation functions were decided using experiments, shown in Table .Table 6TIn this experiment, we used a unified dataset containing four omics data . The datWe first removed the features with unknown labels in the metabolomics (45 unknown features) and lipidomics (2710 unknown features) analyses for data preprocessing. Then we cleaned the data by eliminating features and samples with NA values greater than 20% or zero values. The samples were then filtered to retain only patients with all four valid omic data, with some samples missing some omic data. Eventually, the final omic data was obtained for 100 patients.p value of 0.004.The training dataset does not contain survival status and patient information over a continuous period, so we cannot directly rely on survival time as a severity measure. It requires us to select a value that reflects multiple indicators as an evaluation criterion. In this case, we used hospital-free days at day 45 (HFD-45) . This scAutoencoder We used multi-omics datasets to build COVID-19 diagnosis and severity prediction models, which have high feature dimensions. We used autoencoder, correlation analysis, and elastic net regression to reduce the feature dimension and avoid over-fitting risk. In our work, the data of 100 patients were used as the input of the autoencoder using the preprocessed four-omics data. The four omics data were stacked into a matrix through sample scaling.relu activation function in input and hidden layers, and tanh activation function in the output layer. We used binary crossentropy loss function, which calculates the error between the input data and the output. L1 and L2 regularisation was used to reduce overfitting, where the L1 and L2 parameters were 0.001 and 0.0001, respectively. We used Python (v3.8.16), Sklearn (v1.0.2) and Keras (v2.9.0) to build an autoencoder and saved the value of the bottleneck layer node as a new feature extracted from the original four-omics data. We experimented with different combinations of hyperparameters while constructing the autoencoder model  using the elbow method , which u 20 Fig. . It was In order to clarify the robustness of survival subgroups, we used a series of ML models belonging to supervised learning algorithms to classify the severity of COVID-19. The classification labels used for these ML classifiers were determined by K-means clustering rather than using a single clinical feature. In order to check the effectiveness of the performance of labels and features generated by the autoencoder, we performed 10-fold cross-validation to obtain the average evaluation results.To test the robustness of the subgroups obtained by clustering in terms of prognosis, we used independent proteomic and metabolomic datasets for validation. We obtained top-level different characteristics between the subgroups using analysis of variance (ANOVA). First, we performed Min–Max normalisation of the training set samples to avoid the effect of different feature data sizes on the ANOVA. We selected the top N features that are most relevant to the clustering labels (obtained from the K-means). Depending on the size and complexity of the dataset, we set the default N value to 50 for Proteomics and Metabolomics, and to 100 for Transcriptomics and Lipidomics.We used ANOVA to collect the features with the greatest difference between the two severity subgroups in the training dataset. Among the features of proteomics and metabolomics, we selected the same features of the validation set . We retained these features in the training dataset and generated a dataset for supervised training. To avoid differences in data platforms that result in data on the training dataset and the independent validation dataset, we used the StandardScaler method, which normalises the training dataset and the independent dataset based on the mean and standard deviation of the training dataset .Several evaluation metrics were used to evaluate individual model performances. We used Accuracy, F1-score, C-index and AUC as evaluation matrix. We used 10-fold cross-validation and the average test evaluation results are reported. It provides generalised results, which enhances the reliability.In our work, the c-index is used as an important parameter to measure the classification accuracy of COVID-19 severity subgroups, and we redefined the calculation of the c-index based on the definition and calculation of the c-index. Concordance index (c-index), based on Harrell C statistics , is simiThe experiments are conducted on a CPU with 25 GB RAM and an NVIDIA K80 GPU with 10 GB of memory on a Windows operation system. The work was implemented in Google Colab, with Python (v3.8.16), and other supporting ML libraries, including Tensorflow (v2.9.2), Sklearn (v1.0.2), and Keras (v2.9.0).Additional file 1: Omics dataset.


We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection . The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented. Cancer is one of the most common causes of death (following cardiovascular diseases) and one of the most critical barriers to increasing life expectancy in the 21st century . InternaA pleural effusion is an accumulation of extra fluid in the space between the lungs and the chest wall (pleura). Pleural effusion (PE) can be caused by infections , liver or kidney disease (cirrhosis), congestive heart failure, autoimmune disorders , and/or pulmonary embolism or trauma ,6. MaligIn this light, the spectral differences between normal, lung cancer, and tuberculous cells   [In SERS spectroscopy, such problems as band overlaps, linearity, and interactions exist. To overcome it, the factors or analysis-based components and multivariate calibration methods have already been successfully and extensively used in the quantitative analysis of spectroscopic data ,45,46,47ver 90%) . X. Chenver 90%)  and discver 90%) . Moreovever 90%) .Herein, the surface-enhanced Raman scattering technique combined with principal component analysis as an optical spectroscopy method, with higher sensitivity and chemical specificity than that in conventional Raman spectroscopy, have been used to maximize the variations between the analyzed groups of cancerous and non-cancerous pleural effusion samples. Through this work, the chemometric supervised techniques of classification  and quantification  of obtained SERS results were performed. The reported results indicate that fast, multivariate evaluation of the multiple probes is feasible and may allow for broad application of inaccurate cancer diagnosis, risk classification, and therapeutic strategies.The human pleural effusion samples were derived from fourteen patients with cancer . Histopathological type of cancer was confirmed either by histopathological or cytological examination during the routine diagnostic process. Six samples with non-cancerous lung effusion, considered the control, were collected according to standard best practice and ethics and bioethics guidelines. The information of the sample classification based on histopathological and immunohistochemical staining of pleural effusion is included in −1. SERS signals were detected by a RenCam CCD 1024 × 256 pixels detector. For each sample, 15 SERS spectra were collected with 8 mW of laser power measured on the sample in mapping mode (3 mm × 3 mm). Then, the spectra were collected with 20 s irradiation and 5 accumulations . Each map was collected for 30 min. Platforms for SERS analysis were prepared according to the already published procedure [SERS measurements were performed by Renishaw inVia Raman System, which is equipped with a 300 mW diode laser emitting a 785 nm line. Briefly, the laser-excitation source passes through the line filter and focuses on the test sample mounted on an XYZ translation stage with a 50× Leica N plane EPI objective lens . The focal volume of laser beam in this configuration is 0.5 mm. The scattered Raman signal was recorded by the same lens projecting the beam through a holographic notch filter to block Rayleigh scattering. The grating mesh has 1200 grooves per 1 mm to obtain a visual resolution of 5 cmBefore multivariate analysis, the spectra were prepared using concave rubber-band baseline correction  with Savitzky Golay Filter: 9 points OPUS 7.2. software . Additionally, normalization was performed using the standard normal variable (SNV), and then, Principal Component (PCR) and Partial Least Squares (PLS) as well as regression and Partial Least Squares Discriminant Analysis (PLS-DA) were performed using Unscrambler software . PLS was introduced to the regression tasks, and then, the PLS-DA discriminant analysis was performed. The PLS-DA algorithm was used for predictive and descriptive modeling as well as for discriminant selection of variables. Thus, throughout the manuscript, the PLS as well as PLS-DA data are presented accordingly. For PCR the NIPALS and for PLS the Kernel algorithms, both with random cross-validation method of 20 segments, were used.Additionally, the root mean square errors of calibration and prediction , the coefficients of determination of calibration and prediction , and the receiver operating characteristic (ROC) as well as the correlation accuracy (AUC the area under the ROC curve) were determined.Generally, fluid builds up in the pleural space due to an overproduction of fluid or/and decreased fluid absorption. If the effusion is due to cancer cells in the fluid, it is called a malignant pleural effusion (MPE). Malignant pleural effusions are present in 7–15% of patients with lung cancer, 25% of malignant effusions being due to breast cancer and 10% accounting for lymphomas, including Hodgkin’s disease and non-Hodgkin’s lymphoma. It should be mentioned that any epidemiological data should be interpreted with caution, as they may show considerable local variability due to, e.g., ethnicity, the local burden of various diseases, the age structure of the population, and the availability and quality of the healthcare system . AdenocaFirstly, to find the origin of pleural effusion, the samples were cytologically studied. Based on the characteristic microscopic images, the samples were classified as benign and malignant. The diagnostic yield of pleural fluid cytology depends on several factors, such as the extent of disease, the primary site of malignancy, and the histological type of malignancy (adenocarcinoma is commonly diagnosed compared to squamous cell carcinoma). The high rate of diagnostic yield in adenocarcinomas is due to the fact that it desquamates easily in the pleural cavity . On the Differentiation between benign and malignant pleural effusion allows for early treatment of BPE, which decreases the possibility of disease complications. In contrast, early treatment of MPE may increase the quality of life and survival of patients with advanced malignant diseases. Below, the model of differentiation between benign and malignant pleural effusion fluid based on the SERS data and both PCR and PLS analysis, elaborated upon for clinical samples, are presented.-Non-carcinoma and carcinoma samples causing pleural effusion;-Type of neoplastic illness (squamous cell and adenocarcinoma samples).Studied sample classification obtained from histopathological and immunohistochemical staining is gathered in −1 is from 8% to maximum 15% and above 1500 cm−1 is up to 25%. Additionally, −1 is 15%. As can be seen within one sample, the spectra are very similar to each other. Therefore, the multivariate method is necessary for further analysis, as is done within this work.The resultant averages of SERS spectra gathered for cancerous and non-cancerous pleural effusion samples are presented in −1, 1079 cm−1, 1174 cm−1, 1223 cm−1, 1339 cm−1, 1555 cm−1, and 1586 cm−1. Those bands are assigned to the vibration of proteins, amide III, and guanine in DNA/adenine/TRP (protein). The increased-intensity 1079 cm−1 band was already observed in samples derived from cancer patients [−1. This band might be connected with a biologically active product such as carbon monoxide (CO). The CO is produced during the oxidation of heme catalyzed by the heme oxygenase-1 (HO-1). Recently, a growing body of evidence indicates that the HO-1 activation may play a role in carcinogenesis and can potentially influence the growth and metastasis of tumors [In patients ,59. Thesf tumors . The tenHowever, all observed changes in the SERS spectral patterns are not sufficient for discrimination between the cancerous and non-cancerous origin of pleural effusion, and as such, this excludes the possibility to use those SERS spectra directly as a medical indicator for tumor detection. Therefore, further analyses were performed over the gathered SERS data using the multivariate method (MVA). Firstly, the principal component regression (PCR) was applied over the SERS data, and then, to more strength categorization and quantification of analyzed data, the partial least square (PLS) method was used. Both methods, PLS and PCR, are used to find the best discrimination algorithm to classify the obtained SERS spectra as coming from non-cancerous or cancerous samples as well as from different types of cancer (squamous cell or adenocarcinoma). The main difference between both methods is the validation set considered in the calculation, which influences the possibility of considering the random noise in the PLS method. Therefore, validation is necessary for the obtained results. Usually performed validation, similar to that used herein, divides the data table into a calibration set and a prediction set. To evaluate the accepted models, the root mean square errors of calibration and prediction  and the coefficients of determination of calibration and prediction  are determined. Additionally, the sensitivity and the specificity based on the receiver operating characteristic (ROC) and the correlation accuracy (AUC the area under the ROC curve) are calculated and presented.Herein, all SERS data were divided into a calibration set and a prediction set in approximately 2:1 ratio . The number of components or factors are selected by the algorithm implemented in Unscrambler as the most important for presented differentiation. The calculated PCR and PLS scores in 2D and 3D scatter plots are shown in Let us consider results of PCR and PLS calculated for non-cancerous and cancerous samples. In the presented PCR data, PC-1 explains 48% of the variance in block X, while PC-2 explains 18%, and PC-3 explains only 8% A. In comNext, to find the determination model for SERS spectra gathered for two different types of cancer, namely squamous cell and adenocarcinoma, again, the PCR and PLS were considered B,D. EvidOne of the most unexpected results during analyzed data was scores distribution according to gender. The PLS discrimination for cancerous and non-cancerous samples versus gender are presented in −1, 1142 cm−1, and 1452 cm−1  and at 639 cm−1, 1140 cm−1, and 1452 cm−1 . Those four bands are responsible for the presented differentiation between the samples of the analyzed groups.Moreover, the analysis of the calculated PCs and Fs  of cancerous and non-cancerous samples and type of cancer data revealed a few essential variables at 638 cm445 cm−1 , Table 1−1, 1127 cm−1, 1310 cm−1, and 1432 cm−1 for the F1 loading plots for females and only two variables at 639 cm−1 and 1455 cm−1 for men (−1), proteins or lipids (1133 cm−1), collagen (1319 cm−1), and proteins or/and lipids (1445 cm−1)  see .Additionally, based on the predicted and correlated matrix of PCR and PLS, the receiver operating characteristic (ROC) curve was constructed for studied samples . To deteTo summarize, the presented method of pleural effusion analysis allows for quick and sensitive differentiation among the possible reason for pleural effusion. Furthermore, it shows a great possibility of faster diagnosis results, and finally, it gives a faster implementation of treatment, which in the case of adenocarcinoma, may significantly increase the chances of curing patients and extending their lives.The potential of SERS in combination with PLS as a rapid method of diagnosing lung cancer from pleural fluids and identifying the two most common types of lung cancer was demonstrated for the first time. The PLS method is the best choice in the tested SERS data configurations (cancer vs. non-neoplastic and squamous samples vs. adenocarcinoma). However, the most visible evidence of this, apart from the percentages of the explained variance, is the calculated value of the root mean square error (RMSE) and the coefficients of determination (R2) of the calibration and prediction . In addition, the calculated area under the ROC curve is consistent with the RMSE results and confirms the effectiveness of the selected PLS method. The obtained results prove the feasibility and convenience of using the SERS method together with the PLS analysis as a competitive tool for the detection of lung cancer and even the type of cancer from the pleural fluids. Moreover, a correlation was found between the discrimination of non-cancer and cancer studies and the gender of the patients. Analysis of data in respect to gender allowed for the practical improvement of the obtained results . This suggests that in future research, biological samples should also be analyzed, taking into account the gender of the patients of the analyzed samples.


OPCML-IT2), to be related to resistance to cancer therapies (RAC1P3), or to be involved in several cancer processes such as genome stability (XRCC6P2), tumor growth and metastasis (MIR602) and regulation of gene transcription (NME2P2). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models’ predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients’ groups based on RNA-seq data.Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods  using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner—a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods’ accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes (The online version contains supplementary material available at 10.1186/s12859-022-05104-z. Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. In 2018, it was the third most common cancer, with around 1.8 million new cases and the second most deathly cancer with almost 900 thousand deaths  [CRC is a very heterogeneous disease that can develop via distinct pathways involving different combinations of genetic and epigenetic changes . These gMetastasis is the major cause of death in CRC patients, and approximately 20% of the patients already have metastases at diagnosis . In thisMany studies try to understand tumor biology and mechanisms that lead to metastasis; notwithstanding, the identification of the factors influencing metastatic tumor cells, especially in colorectal cancer, remains poor . ConsequSupervised learning comes as a natural choice for helping in the classification of patients into metastatic and non-metastatic, based on NGS data. Some of the widely used classifiers applied to RNA-seq data are Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM) –14. HoweAnother common way to address the data high-dimensionality challenge is to use classification algorithms that control the model’s complexity through regularization , 17. Oneℓ2-norm)  regulariℓ2-norm) . Networkℓ2-norm) . Severalℓ2-norm) , or fromℓ2-norm) , 22.In sum, several studies demonstrated that using supervised learning methods in microarray gene expression data  is a verIn this work, we try to find a set of biomarkers that may predict the risk of metastasis using transcriptomic data from a cohort of CRC patients followed at the Hospital de Santa Maria (Lisbon), one of the largest hospitals in Portugal.To achieve this goal, we applied and tested different classification methods using transcriptomic data, and proposed a new combined model that showed higher classification accuracy compared to its model counterparts. Altogether, we proposed a new pipeline for the selection of putative biomarkers based on patients’ gene correlation matrices.https://github.com/sysbiomed/iTwiner.git.To identify important genes involved in the CRC metastasis process, several classification methods applied to RNA-seq data were tested. The pipeline of this study is represented in Fig. Primary tumor samples from patients diagnosed with CRC disease from June 2010 to October 2017 were collected as part of a prospective biobanking project approved by the Ethical Committee of Hospital de Santa Maria, all procedures were performed in accordance with relevant guidelines. Patients were followed at the Oncology Division of Hospital Santa Maria, Lisbon, and were treated as per institutional clinical practice in accordance with international guidelines, namely ESMO and NCCN guidelines. Cases were staged according to The American Joint Committee on Cancer (AJCC) staging system, 8th edition, and patients had not received neoadjuvant chemo or radiotherapy prior to sample collection. Whole transcriptome sequencing (WTS) was performed by Illumina Inc.The dataset used in this study comprises 110 samples from early-stage (II and III) CRC patients with both clinical and transcriptomic (RNA-seq) data. This was obtained from two different cohorts of CRC patients from Hospital Santa Maria : Cohort 1: Cohort described in  containiribed in  containiThe clinical dataset descriptive statistics are summarized in Table The original gene expression dataset was comprised of 39,103 variables (genes). After excluding the genes with a constant expression (standard deviation of zero), a dataset with 37,504 variables (genes) was obtained.A preliminary study of the datasets was performed to verify the statistical significance of the differences between factor variables across the P and PM groups of patients using the Fisher’s Exact test, namely to the variables sex, tissue type, stage of the disease, and sidedness. For the age variable, a t-test was used to compare the mean between the two groups. Subsequent survival analysis was performed for each dataset used, with the main goal of studying the time until an event of interest, i.e., death, occurs . Here, wFinally, differential gene expression analysis was performed to identify genes differentially expressed between the two patient groups (P and PM). To perform this analysis, the edgeR R software package was used, employing an FDR  cut-off of 0.05 to identify differentially expressed genes (DEGs). These genes were further used for classification.Classification is a supervised learning method, where the model learns from a set of predefined samples with given class labels (training dataset). The knowledge inferred from this is applied to classify unknown samples (a test dataset) accordingly .In this work, three binary classification approaches were used to distinguish early-stage CRC patients that metastasize from those that did not: 1) classification methods based on a subset of relevant genes (DEGs), 2) classification via regularized logistic regression with embedded feature selection applied to the full dataset, and 3) all classifiers based on the relevant features identified by regularized logistic regression , logistic regression, and random forest. One of the limitations of these methods emerges when using high-dimensional data. Since a high number of features may lead to problems in classification analysis, a smaller subset containing only genes found to be differentially expressed between the groups P and PM was used. The difference in the expression level of genes is found useful in classification in order to identify disease biomarkers . DecisioAnother approach that has been widely used for classification problems in cancer is logistic regression , 36. Thip covariates  of the i-th patient, and The logistic regression model estimates the probability of belonging to a given class . The main rationale, in this context, is to select biomarker signatures that indeed reflect the different correlation patterns between the metastatic vs. non-metastatic early-stage CRC patients.In this work we tested three different approaches to find the best CRC metastasis classifier: 1) Classifiers based on DEGs; 2) Regularized logistic regression applied to the full dataset; 3) Classifiers based on genes pre-selected from regularization (instead of DEGs).As explained above, the original dataset was split into three smaller datasets due to the existing class imbalance. For each dataset used, samples were randomly divided into a training set (for model construction) and a test set , comprising 70% and 30% of the data, respectively. To obtain statistically reliable predictive measurements, 10-fold cross-validation was performed on the training set to optimize the For the EN model the parameter that controls sparsity was set to To evaluate the models’ performance, depending on the class predicted by the classifier and the true class of the patient (non-metastatic - P or metastatic - PM), four different results can be obtained: True positive (TP) - patient predicted as positive (non-metastatic) and the patient was non-metastatic; False positive (FP) - patient predicted as positive (non-metastatic) but the patient did metastasize; True negative (TN) - patient predicted as negative (metastatic) and the patient metastasized; False negative (FN) - patient predicted as negative (metastatic) but the patient did not metastasize. Using these results, the following measures on the test set were used as indicators of the performance of the classifiers: Accuracy (fraction of correct predictions - Acc), number of misclassifications (Miscl), Sensitivity , Specificity  and AUC (area under the ROC curve). The median values of all performance indexes obtained for train and test sets across the 100 runs were used for comparison.To perform the analysis described above, glmnet  package Different gene expression profiles are expected in early-stage patients that will metastasize compared to non-metastatic patients, as a consequence of molecular, biochemical, and genetic variations that make metastatic cells able to migrate from the primary tumor to other body sites . In thisThe data used to perform this analysis is described in Table p value = 0.0091). Also, PM patients showed worst survival probability when compared to P patients, significant in all datasets . Finally, regarding sidedness, no statistically significant results were found. However, for DATASET1 and DATASET2, there was a tendency for the right side to be related with worst survival probability, as shown in the literature [Afterward, to assess if there were differences in the survival probability regarding clinical factors, survival analysis was performed Fig.  for eachterature .p-values between the two tissues was created, for ease of model building and interpretation. The list of ranked genes can be found in Table Differential gene expression analysis was performed in all datasets to find differential expressed genes (DEGs) between P and PM patient groups. In DATASET1, a total of 9533 DEGs were found. Among those, 1589 were up-regulated and 7944 down-regulated in PM patients. In DATASET2, 1840 DEGs were found, 835 up-regulated and 1005 down-regulated in PM. Finally, 138 DEGs were found in DATASET3, 39 up-regulated and 99 down-regulated. Given the high number of DEGs found in each dataset, a smaller gene set containing only the fifty DEGs that exhibited the lowest GBP4, IDO1, IGHV4-34. Interestingly, all these three genes have important implications in immune regulation, highly relevant for cancer progression [LRP4, LGR6, APOL1 and CXCL11 [The DEGs found in common between datasets are represented in Table gression –45. Seved CXCL11 –49.Fig. p-value described above as means to use the same gene dataset dimension as input to all classifiers in all datasets tested. After training the classifiers 100 times, several performance evaluation metrics were calculated in the test set, such as accuracy, misclassifications, sensitivity, specificity, and area under the ROC curve (AUC). The median results of all runs obtained for each dataset in the test set are displayed on Table To classify primary patients that metastasize, five distinct classification methods were used: decision trees (DT), random forest (RF), linear and radial support vector machine , and logistic regression (LR). Due to high-dimensionality problems, the full gene expression dataset cannot be directly used. Therefore, we decide to perform feature selection using only DEGs found between early-stage patients that metastasize and those that do not metastasize. This is a common approach used to reduce feature dimension before classification. Since the number of DEGs found in each dataset was different, we used the 50 DEGs with the lowest The second approach tested to distinguish early-stage CRC patients that metastasize from those that do not, was to use regularized LR with different types of penalization for feature selection: Elastic net (EN) and the correlation-based regularizer iTwiner. The test set results obtained for these methods applied to the full dataset are described in Table The performance of these methods was similar to the classifiers tested above, where higher accuracy in test set was obtained by the iTwiner method (mean The median number of selected variables (genes) by the two methods (across the 100 runs) used to separate the two groups (P vs. PM) was 48 for EN and 38 for iTwiner Table . Also, tRAC1P3 in common to all datasets. This gene is a pseudogene of the Rac family of small GTPase whose role in cancer is still unknown. Regarding iTwiner, six genes were selected by the three datasets tested, RAC1P3, XRCC6P2, EEF1B2P6, HSPD1P7, TRBV11-1, HORMAD2. The majority of these genes are pseudogenes with an unknown role in cancer. However, HORMAD2 has been reported to have tumor suppressor functions, and its expression was seen down-regulated in cancer [HORMAD2 gene was down-regulated in early-stage patients that metastasize  as an alternative to the DEGs  were applied to the two smaller gene sets obtained by regularized LR. To have the same dataset dimension as before, the fifty genes selected by EN and iTwiner penalties ranked in Table Regarding classifiers applied to gene sets based on EN penalties Table , for allAfterward, we tested the same classifiers applied to a different gene set based on iTwiner penalization Table . The besp-value correction was performed to assess the statistically significant differences between the groups , if the selection of genes is based on correlation matrices (iTwiner), the performance of the models increases significantly, leading to the most accurate results. To better visualize these, Fig. CRC is one of the leading causes of cancer-related deaths worldwide, being metastasis the major cause in these patients. Therefore, it is crucial to accurately diagnose CRC at an early-stage and understand the molecular mechanisms underlying metastasis. Several studies have tried to understand tumor biology and metastasis mechanisms by comparing early-stage versus metastatic tumors. We explore the relevance of studying early-stage (II-III) tumors that do not metastasize versus those that metastasize, in three years of follow-up. However, this is not an easy task since the high-dimensionality of gene expression data leads to problems in classification methods. As such, feature selection methods are important for selecting informative genes prior to classification, to improve their accuracy.Here we present two major contributions to the discovery of metastatic biomarkers in CRC based on classification and feature selection. The first contribution is a new network-based feature selection method, iTwiner, that promotes the selection of genes with distinct correlation patterns in metastatic and non-metastatic patients, and has shown to significantly increase the classifiers’ predictive performance. Moreover, the proposed iTwiner regularizer selected the most stable and robust gene sets, including tumor suppressor genes and genes involved in several cancer processes like tumor growth and metastasis.The second contribution proposes using gene sets pre-selected by regularized LR (via EN and iTwiner) as input features in the classification learning task, with proven improved performance compared to using DEGs as features, across many datasets and classifiers tested. Correlation-based penalization via the iTwiner penalty selected the best gene set for accurately distinguishing the two groups of patients, placing iTwiner as a promising strategy in the classification of CRC patients based on RNA-seq data and for the disclosure of biomarkers of CRC metastasis.As future work, other types of classifiers may be tested, such as Gradient Boosting, Gaussian Process or neural networks, and since different hyper-parameter values may affect the classifiers’ performance, a more in depth investigation on optimization and tuning of parameters should be addressed. Also, studying the output of the binary classifiers and comparing those with genes selected by regularization methods would be an interesting next step, followed by gene function analysis to describe the biological role of genes and find potential enriched mechanisms and pathways.Additional file 1. Performance results and comparison of classifiers.