Differences in tissue microbiota-host interaction between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) about recurrence and metastasis have not been well studied. In this study, we performed bioinformatics analyses to identify the genes and tissue microbes significantly associated with recurrence or metastasis. All lung cancer patients were divided into the recurrence or metastasis (RM) group and the nonrecurrence and nonmetastasis (non-RM) group according to whether or not they had recurred or metastasized within 3 years after the initial surgery. Results showed that there were significant differences between LUAD and LUSC in gene expression and microbial abundance associated with recurrence and metastasis. Compared with non-RM, the bacterial community of RM had a lower richness in LUSC. In LUSC, host genes significantly correlated with tissue microbe, whereas host-tissue microbe interaction in LUAD was rare. Then, we established a novel multimodal machine learning model based on genes and microbes to predict the recurrence and metastasis risk of a LUSC patient, which achieves an area under the curve (AUC) of 0.81. In addition, the predicted risk score was significantly associated with the patient’s survival.IMPORTANCE Our study elucidates significant differences in RM-associated host-microbe interactions between LUAD and LUSC. Besides, the microbes in tumor tissue could be used to predict the RM risk of LUSC, and the predicted risk score is associated with patients’ survival. Lung cancer is the most common cancer in men worldwide, which mainly consists of two subtypes, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). NSCLC can be further divided into lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and large cell lung carcinoma (LCLC) . LUAD anIn recent years, the comparisons between LUAD and LUSC have been widely studied at multiple molecular levels. For example, several studies have shown that there are significant mutational differences between LUSC and LUAD, with a significantly higher mutation rate in LUSC , 7. In a8–Fusobacterium nucleatum, which was abundant in cancer tissues from patients who had relapsed from chemotherapy, could trigger cancer in multiple ways and was related to cancer cell invasion and metastasis , methylation, and copy number variation in LUSC patients in combination with the support vector machines (SVM) classifier . In addiTo resolve the above-mentioned issues, for LUAD, 123 paired transcriptome and tissue microbiota samples were collected, including 75 recurrence or metastasis (RM) samples and 48 nonrecurrence and nonmetastasis (non-RM) samples. For LUSC, 110 paired transcriptome samples and tissue microbiota samples were collected, of which 41 were RM samples and 69 were non-RM samples . The priP value of PCoA is 0.001 in the gene expression data and 0.01 in microbiome data) (P = 6.17e−10). Stacked bar charts were used to demonstrate the composition of microbes in cancerous tissue from types of patients at the genus level  was used in gene expression and tumor tissue microbiome data, respectively; we found that patients with LUSC and LUAD were distinguishable (the me data)  and D. Wme data) . Only 15us level . The topus level . These rP = 0.31) suggested no significant differences between the bacterial communities of RM and non-RM in LUSC  . The lack of a significant overall connection between host gene expression and tissue microbiota in LUAD might indicate that, instead of an overall correspondence between the two, it is probable that only a subset of tissue microbes is connected with a subset of host genes in LUAD.Procrustes analysis was performed to find an overall association between host gene expression and tissue microbial composition in LUAD and LUSC. The results of our analysis showed a remarkable connection between host gene expression and tissue microbial composition across subjects in LUSC (= 0.004) . The ManShigella, Staphylococcaceae, Staphylococcus, Pseudogulbenkiania, and Chromobacteriaceae were enriched in RM, while Leuconostocaceae, Acidovorax, Shewanellaceae, Shewanella, and Comamonadaceae were enriched in the non-RM group . DEGs in LUSC patients potentially interacted with differential bacteria in tissues . We visualized all the correlations between taxon abundance and host gene expression in Comamonadaceae formed distinct hubs in the network. The family Comamonadaceae now includes the genera Comamonas, Delftia, and Acidovorax. Previous studies have shown that the relative abundance of Acidovorax was higher in smokers, and squamous cell carcinoma (SCC) tumors from smokers have an even greater relative abundance of these bacteria (Comamonadaceae family and Shewanella genus displayed high neighbor shift (NESH) scores as the “driver” microbes in our network. Equally, we found HSD3BP4, LINC00675, MKRN9P, and MLIP as the “driver” genes , and then we identified eight host pathways that associated with tissue microbes utilizing these selected genes   and B. RShewanella and Comamonadaceae accounts for half of the total microbial feature importance. In addition, we removed the features obtained by NetShift from the classification model and used the same model to build the classification model. We found that the AUC of the prediction model significantly decreased, as shown in Fig. S2B and D. .Furthermore, we calculated the feature importance of each gene or microbiome in the machine learning classification model, as shown in Fig. S2A and C. The importance of each feature is represented by the feature information. As a statistical indicator describing the ability of an attribute to distinguish data samples, the information gain can describe the ability of the feature to distinguish sample data. We can see that among the feature importance values of the gene set used to construct the classification model, the feature importance of the gene obtained by the NetShift method ranks in the top 10. Similarly, among the feature importance values of the gene set used to construct the classification model, the feature importance of Therefore, we believe that the features obtained by the NetShift method have a good performance in predicting the recurrence and metastasis of LUSC patients.P value of <0.001).To investigate the potential relationship between 34RMSig and patients’ survival time, 34RMSig was transformed into a risk-scoring model. The optimal risk cutoff value stratified patients into the high-risk group and low-risk group with different survival times . Among tThe time-dependent receiver operating characteristic (ROC) analysis showed the performance of 34RMSig for predicting 3-, 4-, and 5-year overall survival, progression-free interval, disease-specific survival, and disease-free interval. Compared to 3- and 5-year values, 34RMSig achieved higher AUC scores in predicting 4-year survival status in overall survival, disease-specific survival, and disease-free interval, with AUC values of 0.913, 0.944, and 0.894, respectively.Comamonadaceae.Currently, the treatment methods for LUAD and LUSC mainly include chemical therapy, radiation treatment, and surgical treatment. However, a high proportion (30% to 60%) of patients will develop a local and/or distant recurrence after surgery, resulting in a long-term survival rate of less than 50% for patients after surgery –37. Thou–We established classification models to predict patients’ recurrence or metastasis in LUSC and compared the predictive performance of mRNA and microbiome data in 34RMSig. Results showed that mRNA has a better predictive performance than tissue microbiome, and the prediction of the model was further enhanced when combining two omics data sets, with an AUC value of 0.81 on 5-fold-cv, which suggested that host gene expression and tissue microbiome abundance information were complementary in predicting recurrence or metastasis in LUSC. To further investigate the clinical application of these correlated genes and microbes, we used the 34RMSig to stratify LUSC patients into two risk groups, and the 34RMSig revealed a significant association with survival and could distinguish between LUSC patients with good and poor outcomes.We also identified a set of pathways that were associated with specific tissue microbiome composition in LUSC. These pathways were mostly related to various metabolic and metabolism-related enzymes, and some of them have been previously implicated in lung cancer: for example, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, and steroid hormone biosynthesis. Previous studies found that CYP (cytochrome P450) metabolizes carcinogens to their inactive derivatives but sometimes converts chemicals to carcinogens. Additionally, CYP is also involved in the activation and/or inactivation of anticancer agents, which indicates that the expression of CYP in lung cancer and paraneoplastic tissues may be an important factor in the efficacy of anticancer drugs. CYP24, which is frequently expressed in lung cancer tissues, can convert 25-dihydroxyvitamin D3 and 1alpha into inactive 24-hydroxylated derivatives, and 1alpha and 25-dihydroxyvitamin D3 inhibit the proliferation of cancerous tissues , 32, 38.Over the past few decades, the study of microbes has provided many insights into research on lung cancer. The importance of the interaction between the host and microbiome in the pathogenesis of lung cancer has become increasingly apparent . StudyinComamonadaceae is of interest in our study. Previous studies found that in lung cancer, the relative abundance of the Comamonadaceae in paracancerous tissue was significantly higher than that in cancerous tissue areas, and Comamonadaceae had the highest linear discriminant analysis (LDA) score among several taxa associated with tumor status  versus adenocarcinoma (AD) in non-small cell lung cancer. Of these, Acidovorax is well differentiated between smoker and nonsmoker populations, with higher levels of Acidovorax in the lungs of smokers than of nonsmokers. In addition, Acidovorax and Comamonas were also more abundant in tumors carrying TP53 mutations, the most common somatic mutation in lung squamous cell carcinoma and strongly associated with prognostic survival of lung squamous carcinoma patients, suggesting that Acidovorax and Comamonas are specific for lung SCC with TP53 mutations  that is expressed in many normal tissues. Recently, several studies have shown that MEG3 expression level is reduced in lung cancer, hepatocellular carcinoma, and gastric cancer  was significantly negatively correlated with c cancer –48. In N cancer –.Comamonadaceae in lung squamous carcinoma patients might be able to be described by TP53: in NSCLC patients with deletion of MEG3 expression and relative increase in Comamonadaceae due to mutations in the TP53 locus, there is a negative correlation between MEG3 expression and Comamonadaceae abundance, in agreement with our findings in host gene-tissue microbial association analysis in We speculate that the association between MEG3 and Shigella. LINC00675, also known as TMEM238L, is a long intergenic noncoding RNA found in the 17p13.1-p12 region of the human chromosome. Previous studies have shown an association between abnormal LINC00675 expression and poor prognosis in a variety of cancers. LINC00675 is involved in the proliferation and migration of cancer cells by promoting the activity of the Wnt/β-catenin signaling pathway to activate downstream, targeting c-myc and cyclin D1  . Then, wThe R package ‘vegan’  was usedP value of <0.05 and a log2 fold change of >1 as thresholds for upregulated genes and a P value of <0.05 and a log2 fold change of <1 for downregulated genes. Finally, expression levels with significant differences related to recurrence or metastasis were obtained according to a |log2(fold change)| of ≥1 and an adjusted P value of <0.05.We used the R package “DESeq2”  to identTo assess the overall correspondence between host gene and tissue microbe in LUAD and LUSC, we performed Procrustes analysis in R using the ‘vegan’ package. For each disease cohort, we used Euclidean distance for host gene expression data and Bray-Curtis dissimilarity for tissue microbiome data. The significance of the rotation agreement was obtained using the ‘protest ’ function with 999 permutations. We also applied a Mantel test to verify the overall correlation between dissimilarity matrices of host gene expression and tumor tissue microbiome abundance in each cohort.P value of <0.05 were considered to be statistically significant.Alpha diversity was used to analyze the diversity of the microbial community based on the operational taxonomic unit (OTU) counts in our samples. Additionally, we used PCoA to analyze differences in microbial communities. The LDA effect size (LEfSe) uses the Wilcoxon rank sum test and the nonparametric Kruskal-Wallis rank sum test method to analyze the differences between groups at different levels . An LDA P values using the ‘cor.test ’ function with a two-sided alternative hypothesis. P values were corrected for multiple comparisons using the ‘qvalue’ package in R. Remarkable gene-taxon correlations were demonstrated using ‘corrplots’ in R, where the direction of the correlation is represented by the color and thickness of the line indicating the strength of the correlation. What is more, the asterisk was used to mark a significant gene-taxon correlation. Finally, using Cytoscape v3.9.1 .Correlation analyses were performed between differential tissue microbiomes obtained by LEfSe and DEGs. We computed the Spearman rank correlation coefficients and the corresponding e v3.9.1 , we obtaWe chose Random Forest (RF), Gaussian naive Bayes (NB), and Adaboost (Ada) as classification models for predicting RM status in LUSC patients.x = , whose corresponding set of class labels is C = {C1, C2, …, Ck}, and where P(kC) denotes the prior probability of kC, the conditional probability of x conditional on kC can be expressed as P(kC|x), and according to Bayes’ theorem, the following equation can be obtained:NB classifier is based on classical mathematical theory and has a high practicality. The principle of the plain Bayesian approach to determine the class to which a sample belongs is by calculating the probability that the sample belongs to a certain class. Given data If the individual feature attributes satisfy mutual conditional independence, the above equation can be expressed in the following form:P(kC|x) for each item to be classified, which will be used as the classification prediction result. The NB model has the advantages of simplicity, speed, and robustness in training and testing large amounts of data and thus is widely used in the analysis of bioinformatics.Based on the above calculation, the plain Bayesian finds the maximum G(x) is as follows:The Ada model algorithm starts with the same settings for the training data set and base classifiers and then changes the weight distribution while training to obtain different classifiers, resulting in a combined classifier with improved classification performance. The expression of the Ada model mG(x) denotes the m-th base classifier model in the Ada model, and αm is the weight of model mG(x) in G(x), which measures the importance of the classification result of mG(x) in the final classification result mG(x). The weights of the base classifier αm were updated by iterative operations.Similar to the principle of the Ada model, the RF model trains multiple decision tree models and eventually determines the class to which the sample belongs by considering the classification results of all decision tree models together. Ada and RF models have excellent properties such as good classification, robustness, and low influence by the imbalance of sample categories. The depth of the Ada and RF models and the number of base classifiers (or decision trees) are important parameters that affect how well the models classify.Shewanella genus, Pseudogulbenkiania genus, Acidovorax genus, Shigella genus, Comamonadaceae family, Shewanellaceae family, and Chromobacteriaceae family were used as predictors. The results of the five cross-validations were averaged as the final results of the model. The specific tuning parameters are shown below.We choose to use the 5-fold cross-validation method to evaluate the average prediction performance of the model. The 5-fold cross-validation method involves randomly dividing the data set into five parts with equal sample sizes, using four parts at a time as the training set to train the model, and using the data from the remaining part as the validation set, cycling five times so that each part is used once as the validation set of the model. Fivefold cross-validation maximizes the use of the data set and reduces the overfitting of the training model. One hundred ten LUSC patients were used as the data set for cross-validation, and 27 DEGs and the As for the RF and Ada algorithms, we adjusted the tree number and the maximum depth of the tree. The evaluation metrics of the model include the area under the curve (AUC), accuracy (ACC), the area under the accuracy-recall curve (AUPR), and recall, which are used to evaluate the performance of the machine learning method in binary classification problems. AUC is the area covered under the ROC curve, which is used to measure the comprehensive classification performance of the model, ACC is the proportion of samples correctly predicted by the model to the total sample, and recall is the proportion of RM patients correctly predicted by the model to all RM patients. AUPR is an evaluation metric used to measure the comprehensive classification performance of the sample in the case of unbalanced sample categories.i = β1biomarkeri1 + β2biomarkeri2 + … + βnbiomarkerin, where i denotes the i-th LUSC patient, n is the number of biomarkers, and βn is the regression coefficient corresponding to the n-th biomarker in the survival analysis regression model. We plotted the time-dependent ROC curves for OS, PFI, DSS, and DPI separately by using the R package ‘survivalROC’.We selected genetic and microbial biomarkers that could distinguish RM from non-RM to explore whether genes and microbes significantly associated with tumor recurrence and metastasis in LUSC patients were significantly associated with patient survival. Overall survival (OS), progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI) were chosen as survival data, respectively. The survival analysis regression model was constructed by the ‘coxph’ function of the R package, with biomarker as the model-independent variable and survival time as the model-dependent variable. The risk scores for each LUSC patient were calculated from the survival analysis regression model as follows: risk scoreP value of <0.05 was considered statistically significant.We selected the point on the time-dependent ROC curve with the largest difference between true positives and false positives as the cutoff value of the risk score, and patients above this cutoff value were defined as the high-risk group and those below this value as the low-risk group. The survival curve was performed by using the Kaplan-Meier method and the log rank test was used to compare the difference in survival probability with the R package ‘survival’. A https://github.com/xfchow/gene_microbiota_interaction. Our pipeline is also publicly shareable and available upon reasonable request of the corresponding author.All programming scripts to access, manage, and run data on LUAD and LUSC as well as development of the interaction analysis, ML pipelines, survival analysis, and so forth can be found at our GitHub repository link: https://portal.gdc.cancer.gov and ftp://ftp.microbio.me/pub/cancer_microbiome_analysis/TCGA/.The gene expression, tissue microbiome, and clinical data that support the findings of this study are openly available at


While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated Learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson’s Disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies. In recent years, Machine Learning (ML) algorithms have gained popularity as a possible vehicle for solving many long-standing research questions in the clinical and biomedical setting. Concretely, the adoption of sophisticated ML models can aid in tasks such as biomarker detection, disease subtyping , diseaseAdopting ML methods is constrained by access to high-quality datasets. In biomedical studies, curating such quality datasets is particularly difficult due to the sample collection and processing costs and the barriers associated with recruiting patients who meet a study’s inclusion/exclusion criteria. The challenge of this task is exacerbated by the fact that institutions that typically collect biomedical samples cannot easily share human specimens’ data due to data privacy regulations, such as HIPAA, EU GDPR, India’s PDPA, Canada’s PIPEDA, mandated by medical systems, or at a national, and international level .Federated Learning (FL) is an optimization method for performing machine learning model training among a group of clients, allowing each client to maintain governance of their local data. Initially developed for learning user behavior patterns on personal mobile devices without breaching individual privacy , FL has This work evaluates Federated Learning methods’ practical availability and utility to enable large-scale, multi-institutional, and private analytics of multiple modality biomedical samples. Specifically, we aim to identify the frameworks biomedical researchers can use to perform federated learning in their work, the expected performance changes, and the implementation challenges they may face. We also discuss the opportunities and limitations of applying federated learning in multi-omics, where samples capture a patient’s genomic, transcriptomic features, and clinical and demographic information through the case study of Parkinson’s disease prediction.We use the multi-modal Parkinson’s disease prediction as a case study for testing Federated Learning on omics data. Timely and accurate diagnosis of neurodegenerative diseases like Parkinson’s is crucial in exploring the efficacy of novel therapies to treat and manage the disease. Since the onset of these diseases typically begins many years before any visible symptoms, early detection is difficult to achieve at a clinical level alone. Usually, it requires information inherent to the patient’s biology.  have alrK folds, one of which is used as a holdout test set, and the remaining folds are used for model training and validation. To compute a baseline performance of classically trained, central algorithms representative of methods used in the current biomedical research paradigm, several central ML algorithms are fit to the training set  enable the application of FL with formal guarantees of sample-privacy . Such apOverall, we believe that this work sheds light onto the feasibility, and noteworthy characteristics of applying Federated Learning for omics analysis. Through our experiments, we find that collaboratively trained FL models can achieve high classification accuracy in multi-omics Parkinson’s Disease Diagnosis, and can remain relatively performance despite heterogeneity among client sites. We also find in our evaluation that although FL is a relatively novel research space in bioinformatics, there is sufficient access to open source methods which biomedical researchers may leverage to enable productive collaborations.faraz@datatecnica.com).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Faraz Faghri  and the Parkinson’s Disease Biomarkers Program .The data used in this study was access-controlled from the Parkinson’s Progression Marker Initiative , while retaining known genetic risk SNPs within the region. For RNA sequencing data, all protein-coding genes’ read counts per sample were used to generate a second set of ten principal components. All potential features representing genetic variants  from sequencing were then adjusted for the DNA sequence-derived principal components using linear regression, extracting the residual variation. This adjustment removes the effects of quantifiable European population substructure from the genetic features prior to training, this is similar in theory to adjusting analyses for the same principal components in the common variant regression paradigm employed by GWAS models. The same was done for RNA sequencing data using RNA sequencing derived principal components. This way, we statistically account for latent population substructure and experimental covariates at the feature level to increase generalizability across heterogeneous datasets. In its simplest terms, all transcriptomic data were corrected for possible confounders, and the same is done for genotype dosages. After adjustment, all continuous features were then Z-transformed to have a mean of 0 and a standard deviation of 1 to keep all features on the same numeric scale when possible. Once feature adjustment and normalization were complete, internal feature selection was carried out in the PPMI training dataset using decision trees (extraTrees Classifier) to identify features contributing information content to the model while reducing the potential for overfitting prior to model generation  was also a known clinico-demographic feature of interest. Ashkenazi Jewish status was inferred using principal component analysis comparing those samples to a genetic reference series, referencing the genotyping array data from GSE23636 at Gene Expression Omnibus as previously described elsewhere . Sex wasCompared to the PPMI dataset, PDBP includes an additional 40 genetic features, which are excluded from this study, allowing PPMI and PBDP to have the same feature set. Additionally, the PPMI samples are collected before any medical intervention, whereas the PDBP samples are, in some cases, collected after patient treatment has commenced. Since the PDBP dataset may include artifacts present in sample features that result from disease treatment, the PDBP dataset is used exclusively for evaluation to avoid the possibility of label leakage.The dataset used in this study as the basis for training and as the internal test set is the Parkinson’s Progression Marker Initiative (PPMI) dataset. The PPMI dataset represents a longitudinal, observational study where patients contribute clinical, demographic, imaging data, biological samples for whole-genome sequencing, and whole-blood RNA sequencing. Samples are collected at 33 clinical sites globally and across a time span of anywhere from 5 to 13 years. This dataset consists of 171 samples of case patients diagnosed with Parkinson’s disease and 427 healthy control patients. The PPMI cohort contains newly diagnosed, and drug-naive patient samples. The PPMI cohort contains 209 (36%) female samples, 388 (67%) male samples (Supplementary Table 3).The dataset used in this study for external, out-of-distribution validation is the Parkinson’s Disease Biomarker Program (PDBP). It is a longitudinal, observational study where patients contribute clinical, demographic, and imaging data and biological samples for whole-genome and whole-blood RNA sequencing. The dataset consists of 712 healthy control patients and 404 case patients diagnosed with Parkinson’s disease. Each sample comprises 714 features, including genetic, transcriptomic, and clinico-demographic information collected at the baseline. The PDBP cohort consists of 480 (43%) female samples, and 636 (57%) male samples. (Supplementary Table 4).Each sample consists of 673 features, including genetic, transcriptomic, and clinico-demographic information collected at the baseline. Of the 673 features, 72 originate from genome sequencing data and polygenic risk score, 596 are transcriptomic, and 5 are clinico-demographic. The clinico-demographic features include age, family history, inferred Ashkenazi Jewish status, sex, UPSIT score.To conduct federated model training, the processed PPMI dataset is split into disjoint client subsets, using one of the split strategies, and assigned to a local learner. To train a global model using the data among all federation participants, an iterative optimization process is run for a predefined set of rounds . During We simulate two types of heterogeneity in our experiments by distributing samples from the training dataset using different split methods. The split method uniform stratified sampling implements label, and site size homogeneity. The split method uniform random sampling implements label heterogeneity, but site size homogeneity. The split method linear random sampling implements label and site size heterogeneity .K folds and during experiments that study homogenous federations. Uniform stratified sampling entails sampling the source dataset such that there is a nearly even distribution of samples among each of the N clients, and the ratio of cases to controls across each client subset is equivalent (where surplus samples are assigned to one of the client sites at random). This method was implemented by partitioning the source dataset by phenotype and then, without replacement, assigning 1/N samples of each phenotype partition to each client using uniform random sampling, with the last client receiving any extra samples. In practice, this additional data was less than 10 of samples. Uniform random sampling entails assigning 1/N using uniform random sampling. Linear random sampling entails assigning ci samples to a client site, where the following is true:Local client data sets were formed by applying such sampling techniques to the centralized training data set. Uniform stratified sampling was used to form the i is the index of the client site. As with previous methods, the final client receives any surplus samples left over. The effect of this linear random sampling strategy is that each of the N clients contains an increasing number of samples relative to the previous clients, and each client site contains a random distribution of cases versus controls.In the above formula, C is the number of samples to distribute, in practice the size of the PPMI training set, and In our simulation configurations, federation rounds operate synchronously, and without failure. Hyperparameters that were used to compute the final results are reported in the appendix, including the random seed used for the presented results.K=6, allowing each fold to contain approximately 100 samples. For each cross-validation fold, 1/K of the PPMI samples are withheld as a holdout test set. The remaining training split of K - 1/K samples are further split using uniform stratified random sampling at an 80:20 ratio into training and validation subsets. The evaluation set was used for cross validated hyperparameter tuning in the central and federated models. The PPMI dataset was selected as the training and internal test set due to the fact that patients samples recorded in the PPMI protocol are newly diagnosed and drug-naive. Additionally, by training on the PPMI dataset, the model is developed on patients earlier on in their disease course. This intentional choice was made in the hopes the model would identify other individuals early on in their disease course and prioritize them for follow-up. The PDBP cohort samples are collected within five years after diagnosis, and may be actively taking medications. While the PDBP cohort is larger, because samples are collected several years after diagnosis, and because patients may be actively taking medication, there is a possibility of label leakage, ultimately motivating the usage of the PPMI dataset for training. Due to the similar nature of the PPMI and PDBP dataset, after processing, the PDBP dataset can be used as an external test set, approximating out-of-distribution model performance.We conducted K-fold cross-validation on the PPMI dataset, where The Federated Machine Learning methods implemented in the study utilize the federated aggregation methods FedAvg, FedProx, and the local learner classification methods, Logistic Regression, Multi-Layer Perceptron, Stochastic Gradient Descent, and XGBoost available through Sklearn and DMLC . The aggSupplement 1Supplement 2Supplement 3


Breast cancer is a highly heterogeneous disease that comprises multiple biological components. Owing its diversity, patients have different prognostic outcomes; hence, early diagnosis and accurate subtype prediction are critical for treatment. Standardized breast cancer subtyping systems, mainly based on single-omics datasets, have been developed to ensure proper treatment in a systematic manner. Recently, multi-omics data integration has attracted attention to provide a comprehensive view of patients but poses a challenge due to the high dimensionality. In recent years, deep learning-based approaches have been proposed, but they still present several limitations.In this study, we describe moBRCA-net, an interpretable deep learning-based breast cancer subtype classification framework that uses multi-omics datasets. Three omics datasets comprising gene expression, DNA methylation and microRNA expression data were integrated while considering the biological relationships among them, and a self-attention module was applied to each omics dataset to capture the relative importance of each feature. The features were then transformed to new representations considering the respective learned importance, allowing moBRCA-net to predict the subtype.https://github.com/cbi-bioinfo/moBRCA-net.Experimental results confirmed that moBRCA-net has a significantly enhanced performance compared with other methods, and the effectiveness of multi-omics integration and omics-level attention were identified. moBRCA-net is publicly available at The online version contains supplementary material available at 10.1186/s12859-023-05273-5. Breast cancer is one of the most frequently diagnosed cancers and is the second leading cause of death among women worldwide . Breast In recent years, several classification frameworks to predict the molecular subtypes of breast cancer have been reported. For example, Rhee et al. presented a hybrid approach integrating graph convolutional network and relation network using gene expression profiles and protein–protein interaction networks . Gao et However, classification approaches based solely on single-omics datasets only provide information on a single molecular level. These approaches fail to provide a comprehensive view of the biological processes involved in breast cancer and cannot capture correlations between molecules from different layers . To overRecently, deep learning-based multi-omics data integration has shown robust performance in several applications, including patient clustering, survival analysis , and bioIn the present study, we propose moBRCA-net, an omics-level attention-based breast cancer subtype classification framework that uses multi-omics datasets. Dataset integration was performed based on feature-selection modules that consider the biological relationship between the omics datasets . Moreover, for omics-level feature importance learning, a self-attention module was applied for each omics feature, and each feature was then transformed to the new representation incorporating its relative importance for the classification task. The representation of each omics dataset was concatenated and delivered to the fully connected layers to predict the breast cancer subtype of each patient.Our moBRCA-net framework consists of four modules: preprocessing, multi-omics data integration, omics-level feature importance learning, and classification. The workflow of our model is shown in Fig. The breast cancer (BRCA) cohort datasets were collected from The Cancer Genome Atlas (TCGA) . Three tFor gene expression and microRNA expression data, we first removed genes and microRNAs for which read counts were not available for all samples. After calculating size factors, the read counts were normalized by library size and were log-transformed using DESeq2 . For DNAp-value less than 0.01 was considered as differentially expressed genes (DEGs). We constructed the gene set composed of 1000 DEGs with the highest log(fold change) values and the lowest adjusted p-value.To prevent a possibility of overfitting and expensive computational costs to train the neural network model, we constructed a feature selection module to integrate the multi-omics datasets. First, the genes remaining after preprocessing were evaluated concerning their informativity as a breast cancer signature by performing differential analysis that compared them to matched normal samples using DESeq2. Genes with an absolute value of log (fold change) greater than 2 and an adjusted To integrate the multiple omics datasets based on their biological relationship, we identified the other omics features related to the selected DEGs. Studies have shown that promoter-associated CpGs play important roles in gene silencing, genomic imprinting, and cancerigenesis –30. Aften represents the dimension of the input data, we defined the k-dimensional embedding vector x was transformed to a new representation b is a bias term. To learn the features playing a key role for classifying the breast cancer subtypes and better understand the relative importance of those features, we implemented the omics-level self-attention module into our model. Given a set of original input data  follows :1\documeC represents the number of breast cancer subtypes, and y -based classifiers: Support vector machine (SVM) , Random Overall, moBRCA-net (denoted as ’moBRCA-net (omics-attn)’) significantly outperformed the other ML-based classifiers with an average accuracy of 0.891, F1-score of 0.887, and MCC of 0.831. SVM showed the second-best performance with 0.866, 0.861, and 0.808 for the average accuracy, F1-score, and MCC respectively -based data generation for certain minority classes in the imbalanced dataset improved the classification performance in various domain tasks such as respiratory disease classification , temporaNext, we investigated the performance improvement of moBRCA-net by the introduction of the omics-level attention modules for feature importance learning. We implemented two variants of moBRCA-net, in one a single-attention module was applied to all features at once, and in the other the attention module was removed to directly classify the breast cancer subtypes, which were denoted as ’moBRCA-net (single-attn)’ and ’moBRCA-net (no-attn)’, respectively. tenfold cross-validation was performed to measure performance. Without the attention module, the classification performance was comparable to that of the SVM, showing an average F1-score of 0.864   and RF. Furthermore, we investigated the impact of the CpG clusters by comparing the classification performance of moBRCA-net based on single CpG-based multi-omics integration using different feature selection methods Fig. . InteresTo validate whether utilizing multi-omics datasets could effectively improve the classification of breast cancer subtypes, we compared the performance of moBRCA-net when using different combinations of multi-omics datasets and a single-omics dataset. We denoted gene expression as ’gene’, DNA methylation as ’methyl’, and microRNA as ’microRNA’. In this experiment, moBRCA-net showed a relatively higher performance when trained based on multi-omics compared with single-omics data Table . Multi-op-value To understand how omics-level attention helped improve the performance of the model to accurately classify breast cancer subtypes, we interpreted the attention scores of moBRCA-net. First, to directly compare the abundance difference between the feature subtypes with the highest attention scores, we visualized the normalized gene expression values and beta values of those features obtained from samples of each breast cancer subtype. One-way analysis of variance  was perfStudies have shown that DNA methylation regulates gene expression in breast cancer, with hypermethylation leading to the silencing of regulatory genes, whereas hypomethylation activates genes required for metastasis, consequently resulting in uncontrolled growth and cancer progression . DNA met subtype . STAT5 e subtype . Indeed, subtype . Furthermentclass2pt{minimIn addition, we used the Molecular Signature Database (MSigDB) , 53 collIn this study, we describe moBRCA-net, a self-attention-based breast cancer subtype classification framework based on multi-omics integration. Gene expression, DNA methylation, and microRNA expression were integrated considering their biological relationships, and an omics-level attention module was implemented to identify and learn the important features critical for cancer classification. The model was then evaluated and compared with ML-based established methods. Overall, moBRCA-net outperformed the previously reported models in breast cancer subtype prediction. Moreover, we investigated the effectiveness of each module in moBRCA-net for classification improvement. Attention scores were interpreted to validate whether our model could concentrate on features highly relevant to cancer subtype classification.The major contribution of our study resides within the multi-omics data integration strategy. To maintain the biological relationship between the multi omics features while integration, feature selection module was constructed to identify the informative breast cancer signature genes and the relation between the identified genes and other two omics features were built based on the promoter and the target relationship. Moreover, we employed the omics-level attention module to first train the classifier to capture the relative importance of each feature for each omics data. Features were transformed to new representations considering the respective learned importance. These lead the breast cancer subtype classifier not to ignore the distribution difference of each omics dataset and to integrate multi-omics more efficiently by reducing the dimensionality of each omics. This is also supported by the experiments performed in our study Fig. ” SectionmoBRCA-net showed a high prediction performance, but there are still some bottlenecks that could be further improved. For example, due to the limitation of the computational resources, feature selection was performed to reduce the number of features for training our model. Also, if the model could learn the dependency between the omics features directly via graph network, it could potentially be able to extract useful relations between the features of different omics datasets. In the future, we plan to extend our moBRCA-net platform to utilize graph neural networks.Additional file 1: S1: Table 1. Hyperparmeter optimization results based on the average accuracy for classifying breast cancer subtypes. Table 2. Classification accuracy results with the different number of selected features in multi-omics data integration. S2: Optimization results of the baseline methods with the different combination of the parameters. S3: Accuracy and weighted F1-score results of moBRCA-net, its variants, and the machine learning-based classifiers from the performance evaluation based on 10-fold cross validation in Fig. S4: Average subtype-wise accuracy and weighted F1-score results of moBRCA-net (omics-attn) from the performance evaluation based on 10-fold cross validation in Fig. S5: Average overall and subtype-wise performance results of moBRCA-net with/without data augmentation based on 10-fold cross validation. S6: Accuracy and weighted F1-score results of moBRCA-net using different feature selection methods in the multiomics data integration based on 10-fold cross validation in Fig.  S7: Normalized values of features showing top 3 to 5 average attention scores from each omics. S8: Top 200 features showing the highest average attention scores across patients for each omics dataset. S9: 53 overlapped genes with the gene set collection from MSigDB and PAM50, amont the top 200 genes showing the highest avearge attention scores across patients.


IGF2, VEGA, SLC38A1, and SLC2A3) were seen in age-matched term FGR versus healthy control placentas. Characteristic histopathological changes with specific accompanying molecular signatures could be integrated through computational modelling to predict if the placenta came from a healthy or FGR pregnancy. Our findings yield new insights into the spatial relationship between placental structure and function and the etiology of FGR.Fetal growth restriction (FGR) is a leading cause of perinatal morbidity and mortality. Altered placental formation and functional capacity are major contributors to FGR pathogenesis. Relating placental structure to function across the placenta in healthy and FGR pregnancies remains largely unexplored but could improve understanding of placental diseases. We investigated integration of these parameters spatially in the term human placenta using predictive modelling. Systematic sampling was able to overcome heterogeneity in placental morphological and molecular features. Defects in villous development, elevated fibrosis, and reduced expression of growth and functional marker genes ( The human placenta is a highly specialized organ in pregnancy, whereby normal functioning is critical to fetal development and long-term health. By acting as a functional interface between the maternal and fetal circulation, it is responsible for maternal–fetal substrate exchange, it protects the fetus from immune rejection by the mother, and it secretes hormones, which maintain pregnancy and promote healthy fetal growth and development ,2. This Fetal growth restriction (FGR) is a major cause of perinatal morbidity and mortality. The risk of in utero or neonatal death is especially high amongst growth-restricted fetuses and neonates who are commonly born preterm . In someThe placenta of FGR pregnancies is characterized by reduced syncytiotrophoblast surface area, increased thickness of the exchange barrier formed by the trophoblast and fetal capillary endothelium, and an increase in placental apoptosis ,13,14,15Better understanding of how placental structure relates to functional capacity is further optimized through robust placental sampling protocols. Methodological errors arise from differences in sampling and/or tissue processing, which has previously hampered the reproducibility and possible extrapolation of placental studies ,25. SystWhile analysis of single sites may miss important histopathological and functional changes, multiple-site collection poses analytical challenges, and methods may still fail to identify critical differences within placentas affected by pathology such as FGR. Statistical modelling allows for the exploration and integration of multiple data points from the same sample to provide more accurate interpretation of functional consequences. Moreover, the use of modelling to predict regional and disease-state differences has high translational potential, for example, providing an explanation for why the pregnancy was compromised and which baby may benefit the most from early health monitoring and/or postnatal intervention. With this study we therefore, firstly, sought to better understand if systematic uniform random sampling methods could be optimized to the challenges that FGR placentas pose. Secondly, we sought to characterize the degree of detailed histological and molecular heterogeneity between multiple samples in healthy and FGR placentas and to examine potential links between histological changes and the expression of genes involved in placental formation, transport, and transcriptional regulation, and their relationships with fetal growth/pregnancy outcome. Finally, in a proof-of-principle approach, we integrated these changes into a novel analysis model to better delineate the significance of the placenta in health and disease.Subjects were recruited from University College London Hospital NHS Foundation Trust, London, UK with ethics approval from the South-Central Oxford A Research Ethics Committee (17/SC/0432). After written informed consent was obtained, term placental biopsies were collected from subjects diagnosed with late-onset FGR , and frPlacentas were weighed and sampled within 30 min of delivery. Sampling sites were chosen to be representative of four central and four peripheral regions always relative to cord insertion A. In marTotal RNA was extracted from n = 9 control and n = 6 FGR frozen placental tissues (n = 1 FGR sample was not taken due to >30 min delay in freezing this sample) using the RNeasy Fibrous Tissue Mini Kit  following the manufacturer’s protocol. Briefly, approximately 2 mg of tissue was added to 300 µL of lysis buffer and homogenized using a bead-based technique. Following treatment with proteinase K and addition of 100% ethanol, the homogenate was transferred to the spin columns, washed with supplied buffers and centrifuged to remove contaminants. Purified RNA was then eluted into RNase-free water. Quality and concentration of extracted RNA were determined by Nanodrop .® gene expression assay probes (Thermo Fisher Scientific) with either VIC or FAM fluorophores for 5 housekeeping genes  and 8 genes of interest   that showed no statistically significant differences between cases and controls were included in the housekeeper geomean calculation for normalizing gene expression across samples. The relative expression of genes of interest was then calculated using the 2−∆∆Ct method.Complementary DNA (cDNA) was synthesized using the Applied Biosystems High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) according to manufacturer’s instructions on a thermal cycler. Real-time PCR was performed in duplicate using TaqMan™ Universal Master Mix II, with UNG (Thermo Fisher Scientific) and inventoried TaqMand VEGFA) . Only stFollowing paraformaldehyde fixation, biopsies of 8 sites from each control (n = 9) and FGR (n = 7) placenta were embedded in paraffin using routine histological techniques and sectioned at 7 µm thickness. Sections were rehydrated using xylene and ethanol gradients, stained with hematoxylin and eosin, dehydrated with ethanol gradients and xylene, then mounted with DPX. Slides were then scanned using a Nanozoomer digital slide scanner .Placentas were analyzed blinded to the diagnosis of FGR. To perform stereological analysis in a manner similar to that performed previously , transpaxgbTree, glmnet, and nnet) were implemented using caret package (version 6.0.90). All 27 models were trained with a default set of hyperparameters, cross-validated on 5 folds , with seed parameter set to 123. The performance of models was assessed with receiver operating characteristic (ROC) curves using area under the curve (AUC) with 95% confidence intervals . Results of AUCs were presented in two ways:To evaluate the informativity of different placental sampling locations  and different data types , we studied all their possible combinations with analysis performed in R (version 4.0.2). To analyze several well-established multi-dimensional methods of data analysis, the machine-learning models .p-values .For illustration of interdependencies of features, which are presented in t tests and Mann–Whitney U tests were performed for parametric and non-parametric continuous data, respectively. Categorical data between FGR and controls were analyzed by a 2 × 2 contingency table with Fisher’s exact test. Two-way ANOVAs followed by Sidak post hoc test for multiple comparisons were used to analyze mRNA expression data by pathology and sampling location. Graphpad Prism 6.01  was used for statistical analyses. A p-value of <0.05 was considered statistically significant for clinical characteristics and mRNA expression analysis. To account for multiple testing in histological analyses, a false discovery rate correction at 5% was applied to determine statistical significance.Normality of data was assessed by D’Agostino–Pearson normality test. ROUT testing for outliers was used to identify any outliers, which were then excluded from the analysis. For comparisons between two groups, Student’s We matched FGR cases and controls for maternal age, booking BMI, mode of delivery, and infant sex  with avaComparing stereological findings by sampling site within the FGR or control groups showed no significant differences between samples taken from the central and peripheral regions of the placenta . Hence, IGF2, SLC2A3, and SLC38A1 expression was significantly lower for FGR versus controls, regardless of sampling location , although no significant correlations were detected for the control placenta. Moreover, the expression of IGF2, SLC38A1, and SLC2A3 genes negatively correlated with both theoretical and specific diffusion capacity in FGR, but not control placentas. In contrast, FGR expression of HNF1A showed a positive correlation with specific diffusion capacity in the peripheral region. Positive correlations between the expression of all genes measured and fibrosis and stromal volumes were found for the peripheral region of control placentas, but not FGR placentas. Positive relationships were also seen for expression of VEGFA and HNF1A with the stem villi volume in central sites of control placentas.We next evaluated relationships between placental morphology and the expression of growth factor and nutrient transporter genes within the different sampling sites, and between control and FGR groups using Pearson’s r coefficient . In FGR xgbTree, glmnet, and nnet, this resulted in 27 models for consideration. The binary outcome of FGR was used as an outcome with a chosen set of features  used as predictors. The predictive performance of each model was assessed using a leave-one-out cross-validation (LOOCV) scheme: the prediction was made for each patient  by excluding (withholding) it from the dataset, training the classifier on the remaining (independent) samples, and then generating predictions for the withheld samples using the trained model. Using predictions on the training subset (on each round of LOOCV procedures), the best threshold (corresponding to the best sum of sensitivity plus specificity) distinguishing controls from FGR cases was found and a binary result was calculated for each withheld sample . Through all rounds, LOOCV-procedure binary results were collected for withheld samples and performance was assessed using these sets of predictions. Performance was estimated using areas under the ROC curve (AUCs) in two ways: “AUC for all samples” and “AUC for all patients”, as described in the Materials and Methods section. For each combination of “placenta part + type of measurement + ML model” two such AUC values were obtained. Full results of these analyses can be found in nnet, i.e., the model using simple artificial neural networks for a classification. This model gave the best result for both “AUC for all samples” (0.979) and “AUC for all patients” (0.917). By considering the stereological and qPCR data separately, the stereological data showed a greater predictive power than the qPCR data  and data types , we considered all their possible combinations using predictive modelling (construction of classifiers that would make it possible to distinguish patients in the control group from patients with FGR). As predictive modelling was carried out using three machine learning (ML) methods; PCR data . HoweverPCR data . The resThere was no significant variability in the morphological features of the placenta within and between the peripheral and central sites of the placenta, regardless of whether they came from a healthy or FGR pregnancy. In addition, the findings indicate that alterations in placental morphology may be uniform across the placenta in late-onset FGR pregnancies without other complications, such as pre-eclampsia, that can lead to more variable histological changes across the placenta .Despite established evidence that the placenta drives a number of the ‘great obstetrical’ syndromes, which include FGR, the relatively poor understanding of underlying placental mechanisms makes interpretation of how they contribute to clinical presentations difficult . HeterogMorphological differences between the control and FGR placenta were consistent with findings of others , and indIGF2, SLC2A3, and SLC38A1 was lower in samples taken from both the central and peripheral parts of the FGR placenta, and correlated with volumes and other specific placental features. These data suggest a widespread role for these molecules in placental structure and development in the presence of FGR. Indeed, IGF2 is known to be important for the formation of the placental exchange interface in humans, among other species [SLC2A3 in mice leads to FGR [SLC38A1 is key for system A activity at term [VEGFA was significantly lower in the peripheral region of the placenta in FGR and correlated to the following placental pathology  that might suggest a causal association. This is consistent with other work reporting lower placental VEGFA expression at term in late onset FGR [HNF1A was more highly expressed by the peripheral region compared with the central region in healthy, but not FGR, placentas. Little is known about the function of HNF1A in the placenta, although the human tissue atlas [HNF1A may regulate a large number of genes in trophoblast cells [Our approach of combining morphological assessments with gene expression aimed to identify some key etiological pathways for the pathology seen. Expression of placental  species , and is  species ,45, but  species . Prior ws to FGR  which sus to FGR . Of the  at term . Other w at term . VEGFA wnset FGR ,51, and nset FGR . Our finnset FGR . HNF1A wue atlas  indicateue atlas . Recent st cells  and medist cells , which cUsing a modelling approach in our discovery cohort, we demonstrated the potential of predicting outcome (control or FGR) based on stereology and qPCR data. Stereology and qPCR measurements taken from the peripheral part of the placenta, and analyzed together, have the highest predictive power. To avoid any possible overfitting of the models, we did not perform any feature-selection in advance for tuning of the algorithm hyperparameters. Despite this, we were able to obtain predictions with very high accuracy, as, in fact, for the best model, only 1 sample out of 60  considered was predicted incorrectly. Indeed, this subject was one that was most likely to be misclassified in all of the tested models. Nevertheless, we found that the morphological data clearly shows separation of features for control and FGR placentas and probably, this made it possible to obtain high quality constructed models. For the present study, we used a very strict LOOCV. Whilst modelling predictions were poor when qPCR data were used alone, adding these data to the analysis of stereology data improved prediction, hence confirming the importance of qPCR data and the underlying link of these data with functional features. Whilst our modelling approach shows potential, future work would benefit from utilizing a larger dataset, with extremes of pathology, to validate the model.Overall, our data add to current understanding of placental function through comprehensive sampling of healthy placentas and those affected by disease (FGR) with careful deep histological analysis, integrated with expression of genes involved in key biological pathways regulating placental function. Whilst our modelling approach requires validation with further pathology analysis, it offers the promise of better diagnostic yield and a novel insight into biological pathways that affect fetal growth and pregnancy outcomes by employing multi-disciplinary approaches to deliver integrative mechanistic understanding.


Cancer is a serious public health concern worldwide and is the leading cause of death. Blood cancer is one of the most dangerous types of cancer. Leukemia is a type of cancer that affects the blood cell and bone marrow. Acute leukemia is a chronic condition that is fatal if left untreated. A timely, reliable, and accurate diagnosis of leukemia at an early stage is critical to treating and preserving patients’ lives. There are four types of leukemia, namely acute lymphocytic leukemia, acute myelogenous leukemia, chronic lymphocytic in extracting, and chronic myelogenous leukemia. Recognizing these cancerous development cells is often done via manual analysis of microscopic images. This requires an extraordinarily skilled pathologist. Leukemia symptoms might include lethargy, a lack of energy, a pale complexion, recurrent infections, and easy bleeding or bruising. One of the challenges in this area is identifying subtypes of leukemia for specialized treatment. This Study is carried out to increase the precision of diagnosis to assist in the development of personalized plans for treatment, and improve general leukemia-related healthcare practises. In this research, we used leukemia gene expression data from Curated Microarray Database (CuMiDa). Microarrays are ideal for studying cancer, however, categorizing the expression pattern of microarray information can be challenging. This proposed study uses feature selection methods and machine learning techniques to predict and classify subtypes of leukemia in gene expression data CuMiDa (GSE9476). This research work utilized linear programming (LP) as a machine-learning technique for classification. Linear programming model classifies and predicts the subtypes of leukemia Bone_Marrow_CD34, Bone Marrow, AML, PB, and PBSC CD34. Before using the LP model, we selected 25 features from the given dataset of 22283 features. These 25 significant features were the most distinguishing for classification. The classification accuracy of this work is 98.44%. Blood is the most important component of the human body, consisting of 55% liquid termed plasma that flows freely through a blood vessel. Plasma primarily aims to transport nutrients, proteins, and hormones to and remove waste from the human body. Red blood cells (RBCs), white blood cells (leukocytes), and platelets are the three biological components that can be distinguished by their colour, shape, size, texture, and content (thrombocytes) . When ceResearchers may now simultaneously study thousands of genes that make up a significant part of the genome using microarray technology. The development of this promising technology has generated interest in its prospective uses in clinical diagnostics and pharmacological research. Finding gene subsets, sometimes referred to as biomarkers that distinguish between occurrences with different labels, such as different tumor types, cancer vs. non-cancer, and therapeutic response, is a vital step in these kinds of applications. A supervised learning task is the first that tries to accurately determine its label, such as whether it is either a tumor or normal tissue, given a gene expression pattern. Over the past few years, this strategy has been successfully tested with several algorithms  and has This research is based on linear programming classification of leukemia subtypes that accompany cancer diagnosis. Linear programming (LP) approaches may be able to quickly and precisely identify expression patterns. Microarray data has typically been subjected to linear programming with the two different but complementary goals of sample categorization and gene choice. This study uses feature selection and linear programming techniques to classify types of leukemia based on leukemia gene expression data. Given that the dataset used to evaluate gene expression levels had 22283 genes (columns) from 64 samples, and that this was too large, utilizing a feature selection strategy improved the prediction technique’s effectiveness (rows). As a result, reducing the data quantity contributed to improving classification performance.Our classification study is useful to classify the different subtypes of leukemia represented in chosen dataset GSE9476 on leukemia gene expression from CuMiDa through feature selection methods and machine learning techniques.The remainder of the paper is organised as follows: The literature review is discussed in section II, the proposed methodology with its steps is discussed in section III, the results are discussed in section IV, and the conclusion and future work is discussed in section V.Many researchers have identified and predicted different cancer subtypes using different types of methods. This section discussed the most notable research that made use of gene datasets via machine learning including Linear programming-based leukemia subtypes research. Y. Tang et al.  developeSilva et al. compared three distinct machine-learning models and data mining techniques to diagnose acute myeloid leukemia and acute lymphoblastic leukemia on gene expression data. The primary algorithm was the support vector machine, the second was the artificial neural network, and the third was the machine learning ensemble, which is a collection of various intelligence algorithms . The learning ability and classifying potential of the Ensemble model were consistent, and it performed better than 94% in classifying AML and ALL leukemia types . CuMiDa The proposed methodology for the classification of leukemia consists of the following steps including data acquisition, data preparation, feature selection, and design of the classifier. A detailed flow diagram of the proposed model is given below in The datasets Leukemia gene expression–CuMiDa  has beenIn this dataset, there is a total of 64 leukemia samples including 8 cases of Bone_Marrow_CD34, 10 cases of Bone_Marrow, 26 cases of AML, 10 cases of PB, and 10 cases of PBSC_CD34. It consists of 5 classes, these five types include Bone_Marrow_CD34, Bone_Marrow, AML, PB, and PBSC_CD34.MATLAB R2021a environment for performing tasks and operations on our dataset. This study was implemented by using the open-source platform MATLAB. We displayed the whole data for each class to identify any outliers or abnormalities. Additionally, visualization greatly aids in data understanding and interpretation, allowing us to use the proper machine-learning techniques and algorithms to create computationally robust models. The data is uniform and has few outliers. Leukemia data is split into two parts, testing and training data.After acquiring the dataset the next step was understanding its features and types. The chosen Dataset GSE94769 is a numeric dataset. We must first display the dataset to understand the behavior of the characteristics and make predictions regarding anomalies. For this, we used As the microarrays hold enormous potential for accelerating the discovery of new biological information since they can concurrently measure the expression levels of thousands of genes. One characteristic of microarray data is that there are many more variables ’P’ (genes) than sample size ’N’. As in the case of our dataset, there are N = 64 samples and P = 22283 genes in total. In limiting the size of the feature set, thus we must find an appropriate gene selection strategy for our microarray dataset so that the feature size should be reduced. Total 25 data features have been obtained from leukemia gene expression dataset. These are the most distinguishing features that are useful for classification. As biomedical problems related to genes are complex and it is difficult to build a perfect model, the ideal case gives near about 100% classification accuracy. Different feature extraction techniques were recommended in the literature to improve classification rates and lower processing costs for identifying important genes. So, we improved our results by extracting those features that give better data separability. Reducing the features helped improve prediction performance in terms of speed and accuracy. The process of choosing the best suitable subset of characteristics is known as feature extraction; need to select a subset of characteristics that contributes more to the best classification, hence features should be prioritized according to their importance in the classification problem.The following algorithm was used for feature extraction in this study.y ∈ R64 be a feature vector of the data matrix and z ∈ R64 be the same feature vector in the transformed domain.α and β are constant, and α > βLet z1 ∈ R8, z2 ∈ R10, z3 ∈ R26, z4 ∈ R10, z5 ∈ R10. Vectors z1, z2, z3, z4, and z5 are split of z with respect to the classes labels. Let zi, we defineWe establish maps to segregate z according to the classes. The feature vector z is a noralization form of y.i = 1, 2, 3, 4, 5For N1, N2, N3, N4and N5 are 8, 10, 26, 10 and 10 respectively.Where Now, we concatenate through a non-linear map “g” such that:d be a measure used for the selection of features.i = 1, 2,…, 22283Let xj is the value in the zero vector. We are doing this for every feature.Where 1, n2, n3, n4, and n5 respectively.Consider classes 1,2,3,4 and 5 that have the number of samples nDi where i ∈ C be the corresponding datasets. After feature extraction:Let F = {f1, f2, f3,…. fk}Let F be the set of features extracted. i ∈ C and We define a mapping h such that:i ≠ j and i, j ∈ C.Consider two datasets ij. For Linear separability: We defineWe have to perform two tasks. Firstly, we have to find whether fx ∈ Rk and b ∈ R1 such that:Both classes i and j are linearly separable if u ∈ {1,2,.…. ni} and ∀ v ∈ {1,2,.…. nj}∀ u ∈ {1,2,.….ni} and ∀ v ∈ {1,2,.….nj} We model this problem as an optimization. The Problem is given as follows:yi ≥ 0  and yj ≥ 0 We introduce variables as Such thatWriting these equations in the matrix form:1a has 1 a-times.Vector We introduce constraints in the standard form of an LP from the above equationsThe objective function is defined as:Pij be a place between class i and j (i ≠ j and j ∈ C). The list of planes is given below in Let Let Pij for the entire data from yij. These are considered as bias for the plane Pij.sLet In this section, we provide our findings on leukemia subtypes classification using microarray data. Our method is based on linear programming. The best selected of the observed features are those that are extremely clearly distinct, and these features are more useful for classification and diagnosing the various subtypes of leukemia. From the dataset of 22283 features, we have chosen those features that satisfied the following two goals: 1. Distinguish those traits that make it easier to data separability. 2. Decide which qualities are most useful for classifying new data. Leukemia 64 samples overall, comprising 8 instances of Bone Marrow CD34, 10 cases of Bone Marrow, 26 cases of AML, 10 cases of PB, and 10 cases of PBSC CD34, are included in our dataset. The feature size of our dataset, 22283 genes’ expression levels, is included in the dataset. The gene expression profiles’ values are contained in these characteristics (GEP). Each of these characteristics aids in placing a sample into a certain class. Our dataset includes five distinct classifications or subtypes of leukemia. The proposed model utilizes these five categories—Bone Marrow CD34, Bone Marrow, AML, PB, and PBSC CD34—to identify the class to which each sample belongs. The 22283 features in our dataset contribute significantly to the curse of dimensionality, which is caused by small sample sizes relative to huge numbers of characteristics. When we compute such data, the curse of dimensionality will require our time, memory, and effort. The selected extracted features details have been given in Feature Number is the serial number of features in the whole dataset as the dataset contains 22283 features in total. Probe Set ID is the label of each feature in the used dataset. It is also a unique number allotted to a specific and relevant group of genes in genetic engineering. The results of 25 selected features are drafted in a table given below in The total number of samples in each class and divided into testing and training samples. The training and testing samples provide 60% and 40%, respectively. Pairwise precision classification on training and testing samples yields 100% and 98.44% accuracy, respectively. Pairwise precision for testing samples have been discussed in No stands for Plane Number. Output classification demonstrates class partition.Pairwise precision classification on training samples yields 100% accuracy. Pairwise precision classification on testing samples gives 98.44% accuracy. We improved our results by extracting data separation features. Reduced feature count improved prediction performance in terms of accuracy as well as speed. The confusion matrix is given in The accuracy, precision, recall, and F1 score have been given the The comparison results of the proposed model have been discussed in Targeting particular treatments for various categories of leukemia patients is one of the biggest medical problems. Improvements to classification models have made them crucial for better cancer treatment. In this work, Linear programming computational models were used to establish the diagnosis of various leukemia subtypes, such as Bone Marrow CD34, Bone Marrow, AML, PB, and PBSC CD34. Leukemia gene expression data from CuMiDa was employed. To make our diagnosis computationally fast, we first rescaled the dataset’s 22283 features and then we chose the most important features technique. The most significant 25 features were selected that have high discrimination power. This study improved the accuracy of the dataset by 98%. Linear Programming models play an important role in the classification of leukemia subtypes. Our model’s overall performance was outstanding. This work contributed to the revelation that when leukemia subtypes are accurately classified and data is fitted with high classified accuracy, cure rates increase and unnecessary toxicities decrease. Because the patient will be able to take preventative measures and doctors will be able to spot the condition earlier. In the future, we can predict such data that have more samples and more subtypes of leukemia. So those types that are not addressed in this study can also be addressed. The expansion of datasets, which will give us access to more samples in the future, brings with it some new challenges. We are able to predict more accurate and complicated classifiers. Reducing the number of created classifiers while using a large number of them simultaneously, as is the case with ensembles of classifiers, is thus one of the primary goals for the future. It is challenging to consistently and precisely classify cancerous cells while avoiding overfitting because of a lack of data, digitization problems, and the curse of dimensionality.


Cancer is a disease in which abnormal cells grow uncontrollably and invade other tissues. Several types of cancer have various subtypes with different clinical and biological implications. Based on these differences, treatment methods need to be customized. The identification of distinct cancer subtypes is an important problem in bioinformatics, since it can guide future precision medicine applications. In order to design targeted treatments, bioinformatics methods attempt to discover common molecular pathology of different cancer subtypes. Along this line, several computational methods have been proposed to discover cancer subtypes or to stratify cancer into informative subtypes. However, existing works do not consider the sparseness of data (genes having low degrees) and result in an ill-conditioned solution. To address this shortcoming, in this paper, we propose an alternative unsupervised method to stratify cancer patients into subtypes using applied numerical algebra techniques. More specifically, we applied a label propagation-based approach to stratify somatic mutation profiles of colon, head and neck, uterine, bladder, and breast tumors. We evaluated the performance of our method by comparing it to the baseline methods. Extensive experiments demonstrate that our approach highly renders tumor classification tasks by largely outperforming the state-of-the-art unsupervised and supervised approaches. There a. As a re. Along t. To eval. Extensi for iden.Large-scale cancer -omics studies aim to understand the molecular mechanisms of cancer, and they have demonstrated that cancer subtypes have a strong association with clinical outcomes. Several studies have been proposed to categorize the tumors into subtypes. In literature, cancer-subtyping studies can be basically divided into two categories: unsupervised and supervised techniques. In the context of subtype clustering, the difference between the supervised and stratification is mainly based on the usage of the subtype labels while the underlying algorithms perform smoothing of the somatic mutation data with gene-gene network. In other words, the smoothing is a simulation of a random walk on the gene-gene network from mutated genes, i.e, gene similarity (sharing common neighbors) in the graph is used to gain strength of similar somatic mutation profiles. If the method is using label information by forcing the random walker to walk towards the known label (by putting known subtype into the cluster centroid), these types of smoothing algorithms are categorized as supervised; otherwise, they are known as stratification. That is to say that the supervised classification and clustering algorithms show differences in terms of how they set the random walker to walk.Moreover, different types of molecular data can be utilized for cancer subtype identification problem. Some of these studies stratify tumors using molecular profiles obtained from mRNA expression . In thesHofree et al. introduced an approach named network-based stratification (NBS) to stratify tumors into meaningful subtypes by clustering patients who have mutations in similar network regions . NBS is 2) to classify tumors  into classes or subcategories. Obtaining labeled data is mostly difficult, costly, and time consuming. On the other hand, unlabeled molecular data are easy to access; and thus, unsupervised methods have been employed for the tumor stratification problem. While solely using molecular data has been shown to be effective for tumor stratification, heterogeneity of this data limits the ability of the clustering methods . Thus, aIn this paper, we propose an unsupervised learning method for stratification of cancer into meaningful subtypes, and it integrates somatic mutation profiles with the knowledge of the molecular interaction network. In the subsequent subsections, we first summarize the existing approaches for the unsupervised tumor stratification problem. We then introduce our method’s mathematical background.In the context of biology, various machine learning techniques have been used for different purposes such as diagnosis, prognosis, screening, or treatment in cancer . Among tIn terms of tumor stratification, network propagation has been used for serving the same purpose, i.e., to capture the similarity among the nodes in the molecular network and to smooth the mutation signal across the network . The bas0 (patient-by-gene binary matrix) represents the mutation profile of each tumor. A is a degree normalized adjacency matrix of the molecular interaction network. The parameter α controls the random walker that determines how much a mutation signal should diffuse on the network. It is set in the range of 0 and 1. Iterative computation is performed via t values  until Ft+1 converges. At convergence (Ft+1 ≈ Ft), Ft (propagated mutation profiles) denotes a patient -by- gene binary matrix in which each tumor’s mutation profile has been smoothed across the network. After obtaining the propagated mutation profiles, which might be high dimensional, non-negative matrix factorization is applied to this matrix to reduce its dimensionality and identify clinically and biologically meaningful subtypes (Fsubtypes .Nonnegative matrix factorization (NMF) is one of the dimensionality reduction techniques and it has been used for many learning tasks in which the following constraint holds, i.e., lower dimensional matrices must be positive . In the In this equation, W and H are a decomposition of F (patient-by-gene matrix), which are formed as a result of network smoothing. W (genes-by-k) is the basis matrix and H (k-by-patients) is the patient cluster matrix. Tr represents the trace of a matrix L = D − A is the graph Laplacian of the K-nearest-neighbor network, where D denotes diagonal degree matrix of the KNN network, and A is the adjacency matrix of the KNN network. λ is the regularization constant to scale network regularizer (L) term in GNMF . MultiplThis methodology serves to represent a consensus among multiple runs of the clustering algorithm. Also, it is used to detect the number of clusters in the data and evaluates the stability of the identified clusters. To represent the agreement among the clustering, a consensus matrix (N × N) is defined, where N denotes the number of elements in a dataset. A consensus matrix is calculated for each cluster, and each element in the matrix denotes the proportion of clustering runs in which two samples are clustered together . In the 2 learns the center of each subtype by using subtype information as labels and similarly labeled smoothed profiles are forced to stay in same clusters  as a label set matrix, where K << n we can define the tumor stratification problem as a well-known label propagation algorithm , and unknown labels as YU = . Now, our objective is to determine the set of YU by depending on YL and the graph’s topological structure. To be more consistent with the terminology, we will call YL = F0.Our proposed method could be presented mathematically as following. By considering Flgorithm . To be m0 Є RnxK (gene-by-gene binary matrix) as known labels in our setting, we will define the tumor stratification problem as a label propagation approach. Let F0 Є RnxK denote a label information matrix, where 1 shows the cluster index. In this study, we assume the manifold smoothness of known labels, i.e, the similar nodes in graph should share similar labels; and penalize the sparseness of unknown labels By using the above defined Fus study , mathemaHere, L denotes the graph Laplacian, and it is defined as L = D − A. A is the degree normalized adjacency matrix of the interaction network and D represents the diagonal degree matrix of A.The sparsity of the assignments can be measured as follows:Formalization of the objective function is obtained by combining these two equations. Hence, we have the following objective function:Here, we aim to find the If the Now, by using the above equation, we try to find the clusters of Firstly, inspired by our previous study  we rewriHofree et al. use random walk model to diffuse information about tumor mutations using molecular interaction knowledge in the network. Instead, in this study, we propose the label propagation approach (as presented above) by replacing α(I-(1−α)A) with L + σI, so that we can elude the above-mentioned ill-conditioned problem. In the random walk procedure, the small degree nodes in the gene-gene network also cause the sparsity problem as a result of the ill-conditioned problem. To circumvent this ill-conditioning problem, we shift the Laplacian matrix via σI, where I is an identity matrix. Finally, we define the diffuse strategy of tumor mutations using the knowledge of the molecular interactions as follows:In the In order to evaluate the relationship between the identified clusters and the clinical data, we have performed survival analysis using the “lifelines” package in Python. In order to investigate the association between the subtypes and patient 5-year survival time, Kaplan–Meier survival curves of subtypes were generated and log-rank test p-values were calculated.2 method (supervised) are also presented in Section 3.1. The clinical analysis that we conducted to evaluate whether our method and state-of-the-art methods can stratify cancer types into clinically informative subtypes is given in Section 3.2. We followed the same subsampling procedure presented in NBS , where the subsampling implies the removal of the genes that contain less than ten mutations. Similarly, in our experiments, 80% of the somatic mutation matrix rows and columns were subsampled without replacement. Graph regularized NMF (GNMF) was performed 100 times on the subsamples of the dataset to stratify the input cohort. To generate robust patient clusters, consensus clustering is used, and hence, a final stratification of the patients into clusters is obtained. Aggregate GNMF results of 100 samples were converted into a co-clustering matrix. Each element in this matrix represents the frequency of that tumor pair as assigned to the same cluster, among all clustering iterations.To evaluate the performance of our method, the results are compared against the state-of-the-art unsupervised and supervised methods. Our method was tested on colon , uterine , head and neck (head-neck squamous cell carcinoma), and bladder  datasets obtained from TCGA. The comparative evaluation of our method with the NBS method (unsupervised) is presented in Section 3.1. Our method was also tested on the breast cancer dataset, obtained from TCGA; and the comparative evaluation of our method with the validation performance of NBSIn this paper, we have compared our approach (unsupervised label propagation) with existing supervised and unsupervised approaches.Unsupervised approach: To make a fair comparison with NBS , we evalTo evaluate the clustering performances, we used Silhouette Coefficient , Davies–In this study, each method was run five times. For each run, the Silhouette coefficient and Davies–Bouldin index values were calculated, and the results were displayed using the boxplots. Higher average Silhouette coefficient values and lower Davies–Bouldin index values indicate better clustering quality. Intra cluster distance values  are calculated separately for each extricated cluster. For a better assessment, the calculated values for each cluster were averaged and the results are shown by tables. Tables should be interpreted by keeping in mind that intra-cluster distance should be minimum to obtain the best clustering result. Therefore, the lowest distance in each column is highlighted in bold.In order to visualize the clusters, we used t-SNE (t-distributed stochastic neighbor embedding)  and heatBy applying the NBS and our method to colon cancer data, patient profiles are clustered into three predefined subtypes. The calculated intra cluster distance values  of COAD dataset are presented in Using uterine cancer data, patient profiles are clustered into four predefined subtypes. The calculated intra cluster distance values  of UCEC dataset are presented in Using head and neck cancer data, patient profiles are clustered into four predefined subtypes. The calculated intra cluster distance values  of HNSC dataset are presented in When the NBS method and our method are applied to the bladder cancer data, patient profiles are clustered into four predefined subtypes. The calculated intra cluster distance values  of BLCA dataset are presented in 2), which is the baseline supervised method for cancer subtype identification .Supervised approach: In this section, using breast cancer dataset, we have assessed the performance of our proposed method against the performance of network-based supervised stratification  , which iposed by  using br2, both of which use somatic mutation profiles at the gene level, using the same four datasets that are utilized in (We would like to note that different mutations on the same gene might have different effects. In literature , it has lized in . We plan2. In the future, we would like to work on the effects of node embedding, such as SiGraC, (As another future study, we plan to extend the idea presented in this paper to the node-embedding context, where we have listed the related works as . Since o SiGraC,  on subty SiGraC,  as a futIn cancer, response to treatment varies from patient to patient. This variability emerges from the characteristics of patient tumor mutation profiles, and hence there is an utmost need to cluster patients based on their genomic similarities. Discovering cancer subtypes is one of the active research topics in cancer informatics. To date, various approaches have been developed for cancer subtype identification using many types of omics data. In this paper, a new label-propagation based unsupervised method is presented to stratify tumor mutation profiles into meaningful subtypes. Using various datasets and extensive experimental configurations on these datasets, we show that our proposed approach outperforms the alternative methods of cancer subtype identification by a large margin.