Microsatellite-unstable (MSI) cancers have distinct genetic and clinical features from microsatellite-stable cancers, but the molecular functional differences between MSI cancers originating from different tissues or organs have not been well studied because the application of usual differentially expressed gene (DEG) analysis is error-prone, producing too many noncancer-specific normally functioning genes. To maximize therapeutic efficacy, biomarkers reflecting cancer-specific differences between MSI cancers of different tissue origins should be identified. To identify functional differences between MSI colon and endometrial cancers, we combined DEG analysis and biclustering instead of DEG analysis alone and refined functionally relevant biclusters reflecting genuine functional differences between the 2 tumors. Specifically, using The Cancer Genome Atlas and genome-tissue expression as data sources, gene ontology (GO) enrichment tests were performed after routinely identifying DEGs between the 2 tumors with the exclusion of DEGs identified in their normal counterparts. Cancer-specific biclusters and associated enriched GO terms were obtained by biclustering with enrichment tests for the preferences for cancer type (either colon or endometrium) and GO enrichment tests for each cancer-specific bicluster, respectively. A novel childness score was developed to select functionally relevant biclusters among cancer-specific biclusters based on the extent to which the enriched GO terms of the biclusters tended to be child terms of the enriched GO terms in DEGs. The selected biclusters were tested using survival analysis to validate their clinical significance. We performed multiple sequential analyses to produce functionally relevant biclusters from the RNA sequencing data of MSI colon and endometrial cancer samples and their normal counterparts. We identified 3066 cancer-specific DEGs. Biclustering analysis revealed 153 biclusters and 41 cancer-specific biclusters were selected using Fisher exact test. A mean childness score over 0.6 was applied as the threshold and yielded 8 functionally relevant biclusters from cancer-specific biclusters. Functional differences appear to include gland cavitation and the TGF-β receptor, G protein, and cytokine pathways. In the survival analysis, 6 of the 8 functionally relevant biclusters were statistically significant. By attenuating noise and applying a synergistic contribution of DEG results, we refined candidate biomarkers to complement tissue-specific features of MSI tumors. MSI-CRC has been reported to have a higher tumor mutation burden and a lower copy number than MSS tumors. Analysis of the differential gene expression data for MSI-CRC has indicated that the most frequently altered functional classes are cell cycle, DNA replication, recombination, repair, gastrointestinal disease, and immune response. Likewise, compared with patients with MSS CRCs, those with MSI-high (H) CRC have been found to be characterized by a higher expression of immune-related genes.Microsatellite instability (MSI) is a distinct genetic condition that leads to carcinogenesis in a subset of tumors, particularly colorectal carcinoma (CRC) and endometrial carcinoma.,6,7,9–11 thereby indicating the presence of underlying tissue-specific carcinogenic mechanisms with large effects. Accordingly, biomarkers reflecting tissue-specific carcinogenesis should ideally be identified to enhance current checkpoint inhibitor-based immunotherapy and to develop new therapeutic strategies that can be applied to maximize therapeutic responses.,16Although different MSI tumors are characterized by similar carcinogenesis irrespective of the tissue of origin, there appear to be marked differences in prognosis and the response to therapy, depending on the tissue of origin,,18 Consequently, we have adopted these methods to investigate tissue-specific carcinogenesis in MSI cancers . The methodologies typically applied for the identification of DEGs tend to be very effective when analyzing a single type of condition, such as tumor versus normal tissues. However, many researchers are aware of the methodological limitations of this approach when >2 types of interacting conditions are involved. In our studies, most of the genes identified using standard DEG identification methodologies might be normally functioning tissue-specific genes that are unrelated to carcinogenesis. These issues were addressed in the Cancer Genome Atlas (TCGA) PanCancer project, in which the applicability of methodologies based on orthogonal partial least squares-discriminant analysis was assessed as alternatives to the standard procedures. Biologically relevant DEGs were identified, and many normal tissue-specific genes were removed. If the standard DEG identification procedures were applied, such as DeSeq2 or edgeR, it was found that the list of genes finally obtained was very different and unstable.,21 However, DEG identification in the context of >1 type of condition remains methodologically complex, and the optimal threshold is difficult to determine. Moreover, if a nonnegligible degree of molecular heterogeneity in a sample cohort is anticipated, which is a frequent occurrence in studies investigating tumor samples across different tissue origins, DEGs alone would be insufficient as the main outcome of gene expression profiling.Identifying differentially expressed genes (DEGs) via gene expression profiling is a routine approach used in oncological studies. Unlike clustering, which divides genes or samples into several mutually exclusive subsets, biclustering operates on both genes and samples and produces multiple overlapping biclusters. Given that each bicluster is a distinct fraction of the entire dataset and is potentially functionally correlated, identifying functionally relevant biclusters can partly replace the procedure of DEG identification. However, the typically adopted biclustering procedures produce only a small proportion of functionally relevant biclusters, along with considerable amounts of irrelevant products that require further extensive functional filtering.Biclustering, a technique that is widely used in functional profiling, is a data-mining technique that yields closely related subsets of genes and samples. Data modulated by biclustering can be used as essential building blocks for investigating molecular heterogeneity. Most gene expression profiles are currently analyzed using GO. By investigating the enriched GO terms of a bicluster and combining these results with those obtained based on DEG analysis, a more refined set of biclusters, which are potential biomarkers of interest, can be obtained.Formalized knowledge bases such as gene ontology (GO) are particularly useful in evaluating the functional relevance of biclusters. GO is a highly organized formal representation of gene products developed and maintained by the GO Consortium.In this study, we examined the cancer-specific functions of 2 main types of MSI cancers  using gene expression data from TCGA. To overcome the typical limitations of error-prone DEG identification procedures, we developed a novel method that combines biclustering and DEG identification using enriched GO terms. These refined sets of biclusters reflected more cancer-specific functions in MSI colon or endometrial cancers, and we assessed their clinical significance as potential biomarkers based on survival analysis. In accordance with the policies of TCGA and GTEx, neither ethical approval nor patient consent was necessary for this study. The Recount3 project aims to provide uniformly processed analysis-ready RNA sequencing data from diverse resources, including TCGA and GTEx, using optimized pipelines. Having initially selected the MSI-H cancer and TCGA normal samples (colon and endometrium), these were subsequently combined with GTEx samples, yielding a total of 1319 samples: 873 normal colorectal tissue, 95 colorectal MSI cancers, 194 normal endometrial tissue, and 157 endometrial MSI cancers. Using principal component analysis (PCA), we confirmed that the TCGA normal samples could be combined with GTEx samples without further processing for our research goals, as the bias between GTEx and TCGA normal samples would have a minimal influence on the final results .The workflow of this study is illustrated in Figure  Initially, each DEG between colon and endometrial MSI cancers and between normal colon and endometrial tissues was identified based on the threshold criteria of an adjusted P value < 0.001 and an absolute log fold change > 1. DEGs in normal tissues were excluded from those in tumors to obtain cancer-specific DEGs. Although these DEG identification procedures may have limitations, the crude DEG lists were compensated for in the following steps: biclustering and a GO tree search. Volcano plots of gene expression changes between MSI colon and endometrial cancers were constructed using the Enhanced Volcano Bioconductor package.The identification of genes characterized by differential expression between the colon and endometrial cancers of the MSI subtype was performed using DESeq2-based methods. MoSBi provides an integrated interface for different frequently used biclustering algorithms, including FABIA, ISA, Plaid, and QUBIC, with default parameters for each algorithm. A bicluster is part of the entire dataset that consists of a subset of genes and samples with high correlation, which is defined based on the metrics used. Each algorithm produces different types of biclusters characterized by different features. To verify the similarity of the biclusters and the influence of the algorithm on this similarity, we assessed the Jaccard similarity between any 2 biclusters. In Jaccard similarity analysis, we defined the intersection between 2 biclusters A and B as a set of pairs consisting of gene and sample, where both genes and samples belong to both A and B, and the union as a set of pairs consisting of gene and sample, where both genes and samples belong to either A or B and then applied the definition of Jaccard similarity as follows:We conducted biclustering analysis of the normalized gene expression dataset using the MoSBi Bioconductor package.This value can be obtained in a straightforward manner for each pair of biclusters and is represented as a heatmap generated via hierarchical clustering. On the basis of visual inspection, the influence of the algorithms on Jaccard similarity can be readily identified. Subsequently, we did not perform any algorithm-specific analysis of the biclusters and combined these within a single dataset.q value < 0.001 and odds ratio of either colon or endometrial cancer >1). These procedures accordingly facilitated the selection of biclusters closely associated with cancer. For example, if a bicluster consisted of only 55 colon cancer samples, it would have unique features present in a subset of MSI colon cancers, although not in the remainder of the samples related to the genes in the bicluster. However, the aforementioned example represents an extreme case, and given that our data were noisy, a suitable statistical measure is required. Fisher exact test is a simple statistical method applicable to this problem. The odds ratio shows the direction of data bias, and consequently, if in a comparison between a specific cancer (either colon or endometrium) and other conditions, the odds ratio is >1, it could be considered cancer (either colon or endometrium)-specific.A cancer-specific bicluster was defined as a cluster in which sample distribution was significantly enriched for either MSI colon or endometrial cancer. To select cancer-specific biclusters, we used Fisher exact test . In this regard, Gprolifer2, a widely used functional annotation tool and a programmatic interface using REST API, has previously been implemented, enabling researchers to construct automated analysis pipelines written in R. An adjusted P value of 0.05 was applied to select enriched GO terms for each DEG set and cancer-specific bicluster, and we presented the main results of GO enrichment graphically using AmiGO and REVIGO. The interactive graph created by REVIGO also depends on the European Bioinformatics Institute Gene Ontology Annotation database.To investigate the functional features of our DEGs and biclusters, and to select functionally relevant biclusters, we carried out GO enrichment analysis using gprolifer2.To identify functionally relevant biclusters among cancer-specific biclusters using the GO enrichment results, we developed a novel method based on the relationships between the GO terms enriched with DEGs and those in the biclusters in the GO tree structure. We reasoned that if a bicluster was functionally more relevant to either MSI colon or endometrial cancers than to other conditions, then numerous enriched GO terms in the bicluster would be child terms of the enriched GO terms enriched with DEGs.On the basis of these assumptions, we developed 2 metrics, namely, the term childness score and term-set childness score, the former of which provides a representation of the depth of GO a given term among the DEG GO terms and is formally defined as follows:Term childness = the maximum distance of a query GO term to any parent GO term/(maximum distance of a query GO term to any parent GO term + maximum distance of a query GO term to any child GO term),where parent and child terms indicate GO terms with either parent or child relationships in a type of relation, as either “is_a” or “part_of” relationships in the transition-enabled GO tree, and distance indicates the number of DEG GO terms involved in the transitive path from a query GO term to a DEG GO term. The transitive property indicates that if term A is a parent of term B, and term B is a parent of term C, then term A is a parent of term C. This transitive property also applies to the entire GO tree.The term-set childness score is simply the mean score of the term childness score of terms belonging to a term set, which is generally the result of a GO enrichment analysis. On the basis of the distribution of the term-set childness score of the biclusters, we set the threshold of significance of the term-set childness score to 0.6. Although the threshold of 0.6 was more or less lenient when combined with the other filtering processes, it did not have any substantial influence on the final results obtained.In these schemes, if a bicluster had a term-set childness score >0.6, it was deemed to be functionally relevant. On the basis of the term childness score thus obtained, a gene childness score was also derived as the maximal term childness score of all the term childness scores of all GO BP terms of an annotated gene.By comparing the cancer and noncancer-specific biclusters, we assessed whether the childness score was a reasonable measure of functional relevance. Given that numerous child terms of DEGs were cancer-related and numerous normal genes were excluded from our DEG list, we predicted that childness scores would be higher in cancer-specific biclusters than in cancer-specific biclusters.K = 2) of the PCA results. Survival analysis of the stratified data for each functionally relevant bicluster was performed using the Kaplan–Meier estimator for overall survival. Survival analysis entailed assessing whether functionally relevant biclusters were more significant than other biclusters.To evaluate the functional significance of the selected functionally relevant biclusters as potential biomarkers, we performed survival analysis using TCGA clinical data for colon and endometrial cancers, which were directly linked to the samples in the biclusters. In survival analysis using functionally relevant biclusters, we assessed whether the significance of bicluster genes was limited to bicluster samples, and if not, whether this could be generalized to the entire dataset . Specifically, for each functionally relevant bicluster, we obtained gene expression data consisting of the bicluster genes and total samples of either MSI colon or endometrial cancers, and then for those gene expression data, we performed PCA and stratified the data into 2 groups, and using 2-group stratification, we carried out survival analysis. The criteria used for the stratification using PCA were based on either the median value of the first or second principal component (PC1 or PC2) or K-means clustering , of which, 32 and 9 were enriched in colon cancer and endometrial cancers, respectively. Examples of the cancer-specific and noncancer-specific biclusters are shown in Figure Among the 153 biclusters, we selected 41 cancer-specific biclusters using Fisher exact test . These findings tend to imply that many of the enriched GO terms in the cancer-specific biclusters are child terms of DEG GO terms, thereby indicating that the childness score might have applicability in the selection of functionally relevant biclusters.Using novel childness scores, we sought to select functionally relevant biclusters from the cancer-specific biclusters The childness score reflects the extent to which GO terms in a bicluster tend to be child GO terms among DEGs. When we set the threshold of the childness score to 0.6 according to the distribution of childness scores Fig. A, 8 biclWhen functionally relevant biclusters were further analyzed via hierarchical clustering, the samples (columns) of the biclusters tended to cluster according to the tissue type or whether they were cancerous or normal Fig. B. IntereOn the basis of survival analysis, we assessed the potential utility of functionally relevant biclusters as biomarkers. PCA of the gene expression of bicluster genes in the tumor samples, with tumor samples (either colon or endometrium) stratified into 2 groups according to the PCA results, revealed 6 (75%) of the 8 functionally relevant biclusters to be statistically significant for overall survival in either the colon or endometrial cancer cohort  based on error-prone DEG analysis. By combining DEG analysis and biclustering using the GO graph structure, among >100 biclusters, we identified 8 biclusters specific to the context, 6 of which showed clinical significance, as validated by survival analysis.–40 and we suspect that the lack of research addressing these issues can probably be attributed to the extreme methodological dependence of DEG analysis.To the best of our knowledge, this is the first study in which the differential functional profiles of MSI colon and endometrial cancers have been assessed using gene expression data. To date, most transcriptomic studies on MSI colon and endometrial cancers have tended to focus on the differentiation of MSI from MSS cancers or on molecular subtyping,DEG analysis is the most frequently adopted approach used for gene expression profiling. However, the typically applied DEG analysis pipeline, which has been particularly effective in analyzing samples derived from the same tissue of origin, might be insufficient for identifying cancer-specific genes that are differentially expressed between cancers associated with different tissues or organs. Consequently, this may result in the prominence of false-positive genes representing tissue specificity between normal tissue types. However, to a certain extent, GO enrichment analysis appears to be resistant to the inclusion of false-positive genes, given that GO terms are enriched only when a sufficiently large number of genes are annotated to a particular term. On the basis of these properties, GO can be used to combine DEGs and biclusters, as in the present study. However, despite the robustness of GO enrichment analysis, further research should be conducted to develop methods for producing more accurate DEG results in samples with >1 type of interactions. MSI colon tumors tend to be poorly differentiated, implying that tube lumen formation is relatively weak, whereas MSI endometrial cancer tends to be an endometrial type characterized by preserved tube formation, although poorly differentiated types predominate.,43 These findings thus indicate that tube-forming properties, which may be mediated by the EDAR or SHH protein,,45 are important functional differences between MSI colon and endometrial cancers, of which, EDAR has been established to promote colorectal carcinogenesis via the Wnt/β-catenin signaling pathway. It is notable that in the DEG list, of the 2 genes SHH and EDAR in bicluster 53 annotated to the term “tube lumen cavitation,” only EDAR was included, which would be insufficient to enrich this GO term. Consequently, these important features of MSI tumors would not have been identified based solely on DEG analysis.We anticipate that further investigation of the 8 functionally relevant biclusters identified in this study will provide insights into the distinct pathogenesis and therapeutic strategies for MSI colon and endometrial cancers Table . Biclust It is interesting that SMAD4, a member of the TGF- β pathway, was reported to be associated with a poorer prognosis in MSI-H CRCs, but not in MSS CRCs.,49 In endometrial cancers, however, the differential roles of SMAD4 in the prognosis depending on MSI status were not reported.Frequently identified cancer-related GO terms including “transforming growth factor beta receptor signaling pathway” (biclusters 46 and 47), “G protein-coupled receptor signaling pathway” (bicluster 12), or “tyrosine phosphorylation of STAT protein” (bicluster 21) were also identified in some functionally relevant biclusters. Among these cancer-related pathways, the TGF-β pathway has been found to be more frequently mutated in MSI colorectal cancer than in MSI endometrial cancer.,51 Although, to the best of our knowledge, the MSI-specific features of these pathways have yet to be reported, our novel findings provide evidence to indicate that there might be differences between MSI colon and endometrial cancers with respect to the degree of the contribution of these pathways.The contribution of the JAK-STAT and G protein-related pathways to the carcinogenesis of colon and endometrial cancers is substantial, although little is currently known regarding their differential functions in colon and endometrial cancers. Immune-related GO terms included “antimicrobial humoral response” (bicluster 12), “cytokine-mediated signaling pathway” (bicluster 21), and “T cell activation” (biclusters 46 and 47), and it is conceivable that the fine regulation of these immune-related functions might contribute to the maximal therapeutic efficacy of immune checkpoint inhibitors in MSI tumors.Subtle differences in immune-related functions, which are believed to exist between the 2 highly immunogenic tumors, were also observed in some biclusters. However, further studies are required to determine the “missing link” between spermatogenesis and carcinogenesis.Some enriched GO terms were found to be difficult to interpret despite the rigorous effort. For example, bicluster 89 was enriched with respect to “sperm DNA condensation and “spermatogenesis and exchange of chromosomal proteins, which are not believed to have a direct association with cancer. Despite the lack of interpretability, certain cancer-related genes, such as CDH5, were identified, which are annotated to “spermatogenesis.” However, little is currently known regarding MSI at the DCD site or its gene expression. In the present study, we found that compared with CRC, DCD showed a large fold change (log2 fold change: 11.7) in endometrial carcinoma  gene family characterized by antioxidant properties.Although gene expression profiling is an essential step in functional profiling, caution should be exercised to avoid overemphasizing the importance of gene expression profiling, particularly when the tissues of origin differ, as in the present study. The main functional differences between MSI colon and endometrial cancers could be attributable to multiple factors, including not only tissue properties, which may be reflected by gene expression, but also gross anatomy, histological composition, and physiology, thereby making it difficult to precisely characterize gene expression. Given that this study was primarily based on gene expression profiling alone, further studies will be necessary to investigate more holistic systems, including gene expression, anatomy, and physiology.The limitations of our research are closely associated with those of current bicluster search algorithms. As shown in Figure Fine-tuning data preprocessing prior to running biclustering algorithms could also contribute to enhancing bicluster selection. Specifically, in this study, we did not consider the predominance of normal samples in our dataset, and the data were entered as input data for biclustering without correcting for the predominance of normal samples. The bias in the sample-type distribution toward normal samples may thus have contributed to a preferential selection of normally predominant biclusters. Further studies are accordingly necessary to minimize the impact of sample-type bias on biclustering. Nevertheless, we believe that our crude method is effective, at least to a certain extent. Moreover, the strong cancer-specific features of our functionally relevant biclusters enabled us to overcome sample bias and refine the biclusters, which consisted primarily of cancer samples.Our research also provides a foundation for the integration of multi-omics data using biclusters. By extending our research using the multi-omics data, we would identify the regulators of biclusters at the mutation, copy number variation, or microRNA levels. If the interactions between the regulators and gene expression are proven to be biologically relevant, these findings will validate our observations in the present study. In this regard, we plan to conduct a multi-omics study on biclusters as a separate research project.In conclusion, in this study, we examined the functional differences between MSI colon and endometrial cancers based on gene expression profiling, combining DEG analysis and biclustering in a context-relevant manner. More functionally relevant biclusters that can be used as diagnostic and therapeutic biomarkers were obtained via an indirect combination of DEG results and biclustering. We also addressed the issues relating to measures that could be adopted to enhance our novel workflow. This workflow is not limited to the functional profiling of MSI colon and endometrial cancers, and in conjunction with an expansion of the active applications, we anticipate that a more extensive range of clinically applicable biomarkers will be identified.Conceptualization: Woong Na, Young Soo Song, Sung Hak Lee.Formal analysis: Woong Na, Il Ju Lee, Young Soo Song.Funding acquisition: Young Soo Song.Investigation: Woong Na, Sung Hak Lee.Methodology: Woong Na, Il Ju Lee, Young Soo Song.Supervision: Insong Ko, Mihye Kwon, Sung Hak Lee.Validation: Woong Na, Il Ju Lee.Visualization: Woong Na, Il Ju Lee, Young Soo Song.Writing – original draft: Woong Na, Il Ju Lee, Young Soo Song, Sung Hak Lee.Writing – review & editing: Woong Na, Insong Ko, Mihye Kwon, Young Soo Song, Sung Hak Lee.


S)-beta-macrocarpene synthase, zealexin A1 synthase, polyphenol oxidase I, chloroplastic, pathogenesis-related protein 10, CHY1, chitinase chem 5, barwin, and uncharacterized LOC100273479 were proved to be differentially upregulated under biotic stress condition.Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars and is essential for reliable production in maize. Identifying different gene expression patterns can deepen our perception of maize resistance to disease. This study includes machine learning and deep learning-based application for classifying genes expressed under normal and biotic stress in maize. Machine learning algorithms used are Naive Bayes (NB), K-Nearest Neighbor (KNN), Ensemble, Support Vector Machine (SVM), and Decision Tree (DT). A Bidirectional Long Short Term Memory (BiLSTM) based network with Recurrent Neural Network (RNN) architecture is proposed for gene classification with deep learning. To increase the performance of these algorithms, feature selection is made from the raw gene features through the Relief feature selection algorithm. The obtained finding indicated the efficacy of BiLSTM over other machine learning algorithms. Some top genes (( Maize (Zea mays L.), which originated in Mexico, is one of the most frequent cereals after wheat and rice worldwide2. It has supplied 1 × 109 t of global food since 20133. Maize, also called corn, play an essential role in supporting the increased human population through either direct consumption as dietary products or indirect use as livestock feed. Corn could also be a source of bioethanol production, acting as a renewable substitute for fossil fuel4.Cereals constitute around half of the food production worldwide2.Abiotic stresses, in conjunction with biotic stresses, may negatively affect maize production. Pathogens comprising fungal, bacterial, and viral are principally biotic stresses that cause heavy damage. The reduction in global maize yield due to biotic stresses is approximately 10%. During recent decades, the response of maize to abiotic and biotic stresses has been investigated through high-throughput omics approaches, especially transcriptomics. In this study, the dominant biotic stresses, threatening maize production, are further investigated in transcript levels by meta-analysis and artificial intelligence5. Meta-analysis in the bioinformatics field has been employed to determine differentially expressed genes (DEGs) under different treatments  and uncover top genes producing fundamental molecules in confrontation with stress6.Meta-analysis is a relatively inexpensive approach in which the results of independent studies are combined. The increased statistical power of meta-analysis and its generalizability may bypass the challenges associated with individual variations7. One fundamental challenge of the microarray analysis is a high dimension result. Therefore, gene selection is necessary to remove redundant and irrelevant genes and reduce data dimensions that prevent over-fitted learning models8.A dramatic increase in microarray gene expression data, available via online resources, has been supported thanks to recent advances in high-throughput technologies. Therefore, combining multiple datasets can result in more reliable results. Thus, identifying DEGs under experimental conditions in plant breeding programs can be done using robust statistical methods. Microarray generates the expression of thousands of genes simultaneously with a minimal sample size10.Artificial intelligence-based methods have started to be applied in many different areas to automatically perform tasks such as feature selection, feature extraction, classification, and prediction using existing data. Thanks to various artificial intelligence methods, learning from data is carried out. For this reason, mathematical transformations that modify the input data according to the output data are performed. With Machine learning, the sub-title of artificial intelligence, learning and decision-making can be performed by analyzing raw data. In the analysis process, the feature extraction task is essential regarding classification performance. Deep learning, another sub-title of Artificial Intelligence, is the developed version of Artificial Neural Networks, a Machine learning method based on neural networks. The main advantage of deep learning is that it automatically extracts high-level features without requiring a feature extraction step. While it is much more advantageous to use deep learning in applications with high data, machine learning algorithms can be used for less computational cost. Most of the deep learning-based classification applications use Convolutional Neural Networks (CNN) and need labelled data (i.e. supervised)11 presented a CNN-based architecture called DeepChrome to classify gene expression levels. In addition, the developed architecture could automatically learn combinatorial relationships between histone modification marks. The authors performed experimental studies on the REMC dataset containing 56 cell types. Qu et al.12 benefited from different feature extraction methods such as Auto-cross covariance, Khmer, and Parallel correlation pseudo amino acid composition to identify Pentatricopeptide repeat (PPR)-encoding genes and proteins that play a vital role in plant growth. Then they combined and mixed these features. They reduced the size of the blended features with the Max-Relevance-Max-Distance (MRMD) feature reduction technique. They classified the obtained reduced features with high accuracy with different machine learning methods , and Random Forest (RF)).Chien et al.13 emphasized the importance of altering gene expression with T-DNA in rice functional gene studies. In this context, they developed an application to predict gene expression in T-DNA mutants using machine learning techniques. In the application developed in two layers, they coded nine features with PROMOTER and MIDDLE sequences in the first layer. They used these features with SVM. In the second layer, the minimum redundancy maximum relevance (mRMR) feature selection method and the LADTree algorithm were used to select and analyze the values selected in the first layer. This two-layer application, called TIMgo, showed 99.3% accuracy in its five-fold cross-validation experiment.Recently, artificial intelligence has also gained popularity in genomic studies. Clare and KingThe present study was designed to perform gene selection by artificial intelligence, and its performance analysis. Classification of expressed genes under normal and biotic stress in maize using various feature selection algorithms was evaluated. Furthermore, the performance of the algorithms to deal with the data characterized as high-dimension and low sample-size data was surveyed. In the current study, two novel approaches to mining the gene expression of maize, comprising meta-analysis along with traditional machine learning and BiLSTM methods, were integrated for the first time to recognize the most informative genes in response to biotic stress.https://www.ncbi.nlm.nih.gov/geo/). An Affymetrix platform GPL4302 was applied for data collection, and the search was restricted to the data in CEL format. Raw expression profiles were downloaded by the GEOquery package in R version 4.1.2.Microarray gene expression profiles of the datasets related to biotic stress in maize Table  were sea14. Normalization was conducted to remove technical variations in experimental conditions. The combining of datasets from multiple experiments results in batch-specific bias variations that necessitate being eliminated. Thus, after preprocessing, the datasets were merged, and batch effect removal among different datasets was performed using the ComBat function in the SVA R package15, an empirical Bayes method. RMA and ComBat functions were performed using default parameters considered in the corresponding packages.All raw gene expression files were quantile normalized and background corrected using Robust Multichip Average (RMA), which is an efficient way in the affy Bioconductor package18. This study also compares deep learning and machine learning methods in terms of maize gene recognition performance. In this context, the recognition performances of BiLSTM, which is a modification of Deep Learning-based Recurrent Neural Networks (RNN) networks, and SVM, KNN, DT, NB, and Ensemble methods, which are traditional machine learning algorithms, will be evaluated. This section contains information about these artificial intelligence methods.Analyzing the data obtained by laboratory or different sensors, and accordingly, classification and prediction applications have been a research area for a long time. In such studies, instead of an expert's decision, the development of expert devices provides faster and easier solutions. In this sense, thanks to artificial intelligence, it is aimed for a machine to learn or solve a machine learning algorithm, e.g. Naive Bayes (NB), K-Nearest Neighbor (KNN), Ensemble, Support Vector Machine (SVM), and Decision Tree (DT), etc. have been extensively used. These algorithms provide classification and regression outputs by analyzing the input data. Machine learning is a rapidly evolving field with various applications in computer science. Deep learning, which is a branch of machine learning, has become easily applicable thanks to the developments in computer technology. In addition, deep learning-based data analysis applications provide superiority to traditional machine learning methodsMachine learning and deep learning algorithms can be used in applications such as diagnosing plant diseases, determining irrigation and fertilization time, classification of genes, classification of different cultivars of fruits and vegetables in agriculture field.19. For this reason, before the use of artificial intelligence techniques, the min–max normalization technique defined in Eq.  to learn simple decision rules from data features. NB23, is a supervised learning algorithm that statistically distinguishes data according to the Bayesian method with the assumption of naive independence. Ensemble Learning24, combines the multiple learning models. That is, it improves machine learning results by blending the predictions of several machine learning-based algorithms28.SVM30. This study also gives selected features to machine learning methods. A Relief feature selection algorithm31 is used for feature selection. The general representation of machine learning-based gene classification is shown in Fig. In the experimental study, a large number of genes 17,734) are present in the merged dataset, which is a matrix of gene expression values in maize under control and stress conditions. However, a large number of features often necessitate feature selection. Because raw data may contain irrelevant and unnecessary information. This negatively affects the learning success of the learning algorithm. Thanks to the feature selection algorithms, the size of the training samples is reduced. As a result, the computational burden and complexity of classification algorithms are reduced. Feature selection allows machine learning algorithms to focus on the most relevant features, therefore, feature selection is crucial for pattern recognition and machine learning applications7,734 are33. Although RNN is suitable for short-term dependent problems, it fails for long-term dependent problems34. RNN models suffer from the problem of loss of prior information over time when processing long interrelated sequences. Especially as the depth of the network increases, this problem prevents the network from learning. This problem in the training process is also well-known as the vanishing gradient problem. Hence, Long short-term memory (LSTM), a variation of RNN, was proposed byHochreiter and Schmidhuber35 to solve the vanishing gradient problem. LSTM performs better than RNN on long-time series data in the training process because it has a memory cell structure to store previous information.In data-driven artificial intelligence algorithms, traditional methods such as ANN, SVM, and KNN have provided successful applications by modelling complex relationships between data. However, these methods do not consistently achieve global optimization. Especially with time-dependent data, traditional methods such as ANN are not capable. In this context, a Recurrent Neural Network (RNN), which is an advanced version of ANN, takes into account temporal dependence while extracting features from the data. In short, RNN is a kind of enhanced ANN that can convert time-related information into features36 developed LSTM by adding a forget gate. LSTM uses gates and memory cells so that the error gradient decreases over time. These cells ensure that the error value is preserved in the backpropagation step and previous information is remembered. In this way, previous information and existing data can be associated. The structure of LSTM has one memory cell . Detailed results are presented in the sub-headings.In this work, we investigated the gene expression profiles of maize samples in control and biotic stress via several artificial Intelligence algorithms. The entire procedure is presented in Figs. 15In machine learning algorithms, 80% of the data set was used for training and the remaining 20% for the test phase. To select appropriate and high-performance machine learning algorithms, the classification of the maize samples according to their gene expression under control and biotic stress was explored Table . The claAmong the investigated algorithms, while Naive Bayes and Tree performed the worst with an average accuracy of 75%, Ensemble displays the highest accuracy with an average of 85.71% Table . AccordiThe Tree algorithm turned out to be the weakest developed machine for the detection of the negative sample with 63.64% specificity. In contrast, the highest average specificity of 90.91% was obtained by Ensemble, followed by SVM, KNN, and Naive Bayes algorithms. Among these algorithms, Ensemble brought about the highest average precision, with an average of 93.33%. The Tree algorithm performed the weakest in terms of precision, equal to 77.78% 46 to determine the biological processes and pathways controlled by each gene.Among the 17,734 genes in our data set, the 10 most effective genes in the classification of controlled and biotic stressed genes were determined by the Relief feature selection algorithm. We compared the 10 top genes identified by the Relief feature selection algorithm and the list of the genes that were differentially expressed  turned out to be involved in the defense response and diterpene phytoalexin biosynthetic process.LOC100286331, downregulated during biotic stress in maize, functionally was connected to catalytic activity. No biological processes and pathways were identified in the case of the uncharacterized downregulated gene LOC100282059.Gene This study investigated the efficacy of machine learning and the BiLSTM network to construct an optimal classifier that can classify maize samples at the transcriptomic level in control and biotic stress condition. Moreover, 10 top genes were selected based on the Relief feature selection algorithm, listed in Table 7. In this study, gene expression metrics of 14 datasets were merged using meta-analysis. Then, several algorithms of machine learning and BiLSTM were applied to classify maize samples based on the gene expression data. It was concluded that BiLSTM performed better than the machines which were trained.Gene expression datasets are High Dimension and Low Sample Size (HDLSS) and less research has been done on machine learning models in this field. Hence, Deep Learning is being investigated on a multi-omics dataset47. The classification models discussed in this research include SVM, KNN, Naïve Bayes, Tree, Ensemble, and BiLSTM.Machine learning has recently been used in bioinformatics research. The Machine Learning algorithms can be divided into Statistical Learning (Naïve Bayes), perceptron-based algorithms , and logic-based algorithms 48. The KNN algorithm assumes that similar things within a dataset exist nearby. Although perception and implementation of the KNN algorithm are easy, the fundamental principle in choosing the value of k is a shortcoming. As well, it is sensitive to the function of similarity or distance. A Decision Tree is a decision support tool of nodes and branches. Though it is mainly applied due to its effectiveness and calculation speed, this algorithm is highly disposed to underfitting and overfitting to fit the model49. A random forest consists of a group (an ensemble) of individual decision trees. The Random Forest is one of the most powerful machine learning algorithms that can be used for classification. Naïve Bayes is the statistical classification model established based on the assumption of independence of all features in the dataset.The binary classification base on SVM depends on forming a hyperplane employing the kernel function. A paramount preference for adopting SVM is tackling the outliersGenerally, neural networks and SVM perform better for continuous and multi-dimensional features. On the other hand, the logic-based algorithms e.g., the rule learners and decision trees, have better performance for categorical or discrete features. One way to identify the algorithm's outperformance over the other is through the validation process and estimating the model's accuracy. Accuracy was considered as the notable criterion to evaluate the quality of predictive models measuring the ratio of evaluated correct predictions to the total number of cases. While the accuracy obtained through the BiLSTM model was 92.86%, Ensemble was the most accurate model for the classification of maize samples based on gene expression, having high accuracy of 85.71% in control and biotic stress conditions. Moreover, sensitivity, specificity, precision, F1-Score, and MCC were also implemented to evaluate the models.TPS11 (LOC103641097), encoding bicyclic sesquiterpene (S)-beta-macrocarpene, has been reported to contribute to the regulation of plant defence. Köllner et al.50 indicated the expression of TPS11 in the root of the maize resulted in the enzyme production of (S)-β-macrocarpene and (S)-β-bisabolene. It has been reported that the expression of terpene synthases TPS10, TPS11, and TPS21 may support the terpenoid production and serve as resistance to insect attacks51. Therefore, the expression pattern of such a gene can be a potential distinctive biomarker for identifying the tolerant genotypes against biotic stress in maize.The gene LOC100283469 in abiotic stress-tolerant has previously been reported. Dong et al.52 reported that CHY1 mutants showed reduced cold tolerance in Arabidopsis. Expression of CHY1 can lead to hydrolysis of HIBYL–CoA to CoA and b-hydroxyisobutyrate throughout Val catabolism53. Wang et al.54 confirmed that significant enrichment in pantothenate and CoA biosynthesis, besides the valine, leucine, and isoleucine biosynthesis pathways, can be significantly induced by salt stress. To the best of our knowledge, here is the first report of CHY1 as the regulatory gene in biotic stress conditions.The role of Magnaporthe grisea infection in rice. As well, this gene was observed to be responsive in the presence of at least one of the abiotic stress, including drought, salt, cold, wounding, heat shock, and ultraviolet55.Gene LOC103652814 encodes BARWIN, which is a conserved domain of PR4 protein and its function in plant response to biotic and abiotic stress, has been identified. PR4 protein expressions are induced by LOC542525 encodes chitinase chem 5, which may be involved in several growth processes and stress responses. Chitin is a component of the fungal cell wall; however, plants do not produce chitin. Hence, it has been indicated that plant chitinases hydrolyze the cell walls of pathogens and release elicitors, pathogen signal metabolites recognized by plant cells, which may trigger plant defences56. Infection of the plant tissue with fungal pathogens can induce the chitinase expression in many plants56. It was indicated that overexpression of the chitinase could enhance protection against fungal infection in transgenic tobacco57. López and Gómez-Gómez58 demonstrated the promotion of the transcription of some chitinase genes under various abiotic stresses, including drought, cold, and salt58.The gene LOC100280981 upon either the attacks of pathogens or environmental stress identified the involvement of Pathogenesis-related protein in plant defence mechanisms59.Expression of gene LOC103641120 (PPO) is a ubiquitous metalloproteinase in plants with a mediating act in plant resistance against biotic and abiotic stresses60. To understand the expression of PPO in tomato under water stress, two members out of the seven-member PPO gene family were found upregulated in abscission zones of leaf petioles61.62, who determined the transcriptional profile of DEGs in chickpea in response to drought, cold, and high-salinity stress. They reported the repression of LOC100286331 (DY396338) in roots, leaves, and flowers of the tolerant genotype, while DY396338 was upregulated in the roots of the susceptible genotype. Downregulation of LOC100286331 upon biotic stress in maize may explain that plants avoid death through a tolerance mechanism.Our results were in line with Mantri et al.LOC100273457 produces zealexin A1 synthase, which is involved in the production of the antifungal phytoalexin zealexin A1. This enzyme oxidizes (S)-beta-macrocarpene through intermediates of alcohol and aldehyde to form zealexin A163. Shen et al.64 demonstrated the role of LOC100273457 in increased chemical defence while occurring the blast fungus in transgenic rice. They noted LOC100273457 might produce 15,16-epoxy-syn-pimaradien-19-ol to inhibit the germination of spores and the formation of appressorium of Magnaporthe oryzae in rice64.Gene In this contribution, an integrated meta-analysis and machine learning approaches, as well as deep learning, were adopted to classify maize samples based on the gene expression profiles under control and biotic stress. Furthermore, gene ontology and KEGG pathways of top 10 genes, selected based on the Relief feature selection algorithm, were performed to determine underlying mechanisms in biotic stress conditions. As a result, the superiority of BiLSTM over other machine learning algorithms was demonstrated. Moreover, the results of gene selection indicated several potential resistance genes, among which (S)-beta-macrocarpene synthase, zealexin A1 synthase, polyphenol oxidase I, chloroplastic, pathogenesis-related protein 10, CHY1, chitinase chem 5, barwin, and uncharacterized LOC100273479 are more important than others.


With the rapid development of multi-omics technologies and accumulation of large-scale bio-datasets, many studies have conducted a more comprehensive understanding of human diseases and drug sensitivity from multiple biomolecules, such as DNA, RNA, proteins and metabolites. Using single omics data is difficult to systematically and comprehensively analyze the complex disease pathology and drug pharmacology. The molecularly targeted therapy-based approaches face some challenges, such as insufficient target gene labeling ability, and no clear targets for non-specific chemotherapeutic drugs. Consequently, the integrated analysis of multi-omics data has become a new direction for scientists to explore the mechanism of disease and drug. However, the available drug sensitivity prediction models based on multi-omics data still have problems such as overfitting, lack of interpretability, difficulties in integrating heterogeneous data, and the prediction accuracy needs to be improved. In this paper, we proposed a novel drug sensitivity prediction (NDSP) model based on deep learning and similarity network fusion approaches, which extracts drug targets using an improved sparse principal component analysis (SPCA) method for each omics data, and construct sample similarity networks based on the sparse feature matrices. Furthermore, the fused similarity networks are put into a deep neural network for training, which greatly reduces the data dimensionality and weakens the risk of overfitting problem. We use three omics of data, RNA sequence, copy number aberration and methylation, and select 35 drugs from Genomics of Drug Sensitivity in Cancer (GDSC) for experiments, including Food and Drug Administration (FDA)-approved targeted drugs, FDA-unapproved targeted drugs and non-specific therapies. Compared with some current deep learning methods, our proposed method can extract highly interpretable biological features to achieve highly accurate sensitivity prediction of targeted and non-specific cancer drugs, which is beneficial for the development of precision oncology beyond targeted therapy. In the last few years, due to the continuous development of high-throughput bio-data and bioinformatics technologies, people have paid more and more attention to analyze tumor biomarkers and drug targets. The use of genomic data to guide the treatment of cancer patients represents the central principle, which matches patients to specific tumor types and treatments based on the molecularly targeted drugs  Kumar-S Chen et. ResearcAccording to the previous summary, there are usually two computational and analytical approaches for predicting drug response. The first one is using regression approaches to predict the value of the evaluation criteria of cell lines to drug response, and the second one is classifying the sensitivity of each drug on the basis of cell lines . Choi etin vitro drug sensitivity of cell lines to predict clinical drug response  . Models Researchers have already proposed some multi-omics machine learning and deep learning methods for drug sensitivity prediction. However, the biomolecular data are often high-dimensional, e.g., methylation data may be 400,000 to 500,000 dimensions while the sample size is only about 1,000. These methods may suffer from overfitting problems and have difficulties in fusing multi-omics data. In addition, the interpretability of deep neural networks is relatively low, and biomedical methods lacking interpretability make it difficult for the reliable diagnoses of doctors. Moreover, the accuracy of these existing models also has some room for improvement.In response to these challenges, we proposed a novel multi-omics drug sensitivity prediction model (NDSP) based on deep learning and similarity network fusion approaches. The model extracts biomarkers using an improved sparse principal component analysis (SPCA) method for each omics data, and constructs sample similarity networks based on the sparse biomarker matrices, which greatly reduces the dimensionality of multi-mics data and weakens the risk of overfitting in the training process of deep learning. Finally, the fused similarity networks are put into a deep neural network for training and the model can make full use of the high integrability and interpretability of the similarity networks. Compared with some current deep learning methods, our proposed model has the ability to handle high dimensional data and highly interpretable feature selection capabilities. More importantly, the model has higher prediction accuracy than existing models for both targeted and non-specific therapeutics drugs, which is beneficial for the development of precision oncology.in vivo. The experiments proved that ridge regression models trained on GDSC gene expression data could be translated to clinical trial data of Erlotinib, Docetaxel, Bortezomib, and Cisplatin. The paper also indicated that the inclusion of non-breast cancer samples in model training process improves the predictive accuracy of the final model compared with the models trained on breast cancer cell lines only  machine learning approach to identify the biomarkers of drug sensitivity using gene expression data. The authors applied the netBite model to a set of GDSC data for 50 anticancer drugs, where Linifanib was able to achieve an accuracy of about 0.7, and demonstrated that netBite outperformed Random Forest in predicting IC50 drug sensitivity, but only for drugs targeting membrane receptor pathways (MRps): iGfR, RtK and eGfR signaling pathways . Gillehenes only . This genes only   . LearninDing et al. proposed a data-driven precision medicine approach to learn new biological features from omics data to address the dimensionality challenge. The copy number variation, mutation, and gene expression data were concatenated. The variance-based mixed-fit feature selection was performed using the original omics features as the input to the elastic network approach to predict the binarized IC50 values . Chiu etAlthough machine learning methods can handle large-scale data, they are usually considered as black boxes that do not explain well the favorability of specific features for prediction. Interpretability is particularly needed in clinical treatment. Patient similarity networks, a framework that excels in integrating heterogeneous data, handling sparse data, and generating interpretable models, has been applied to several biological fields with good results  Wang et. Pai et The existing deep learning approaches based on multi-omics data still have four major challenges. First, learning new information features from omics data is a key step for model-based drug sensitivity prediction. However, biomolecular datasets tend to be high-dimensional, i.e., with a large number of features and a small number of samples. There is a significant risk of overfitting using deep learning models. Second, deep learning models are a black box, and researchers need to spend a lot of effort to explain what role specific features play in prediction. The black-box approaches are difficult to succeed in the clinical setting because physicians must have an understanding of the underlying relevant features of the disease in order to make a confident and reliable diagnosis. Third, how to integrate different data types is a key challenge in multi-omics analysis, and the main ways are early integration and late integration. In the previously mentioned models that fuse the feature representations learned from each omics before classification, a large number of unaligned gene points are inevitably discarded actively to facilitate feature fusion, leading to data loss problems. Fourth, the results of existing multi-omics drug response prediction methods are unsatisfactory, and there is space for improvement.In this study, we utilize the available oncology therapeutic genomic data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. This dataset is widely analyzed by statistical and machine learning approaches for drug sensitivity prediction. For example, cell line similarity and drug similarity based models , quantitWe select mutation data, cell line annotation and drug IC50 data from GDSC, including targets, signaling pathways, point mutation and copy number variation information and IC50 values of some genes, and several phenotypes for 518 oncology drugs in 988 cell lines. For drug sensitivity study, we select 35 drugs from the GDSC database as experimental subjects, including 14 FDA-approved targeted therapeutics, 16 drugs with clear targets but not yet approved by FDA, and 5 non-specific cancer therapeutics without targets, as shown in https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3610/. The genomic signature of each cell line contains RNA-Sequence values for 44,421 probes, which is known as whole transcriptome shotgun sequencing (WTSS). It contains transcriptional analysis of 1,000 human cancer cell lines to explore questions such as the state of genomic signature on drug response and whether genomic alterations synergistically explain more of the variation in drug response. RNA Sequencing has been considered an effective method for gene discovery, helping to view different transcripts of genes, post-transcriptional modifications, gene fusions, mutations/SNPs, changes in gene expression over time, and differences in gene expression in different groups.The RNA-Sequence data is downloaded from the European Bioinformatics Institute (EMBL-EBI): https://cellmodelpassports.sanger.ac.uk/downloads. Copy number aberration exists in DNA fragments of natural populations and is a common form of structural genomic variation. Abnormal DNA copy number variation is an important molecular mechanism for many human diseases such as cancer and hereditary diseases. Deletion fragments may contain oncogenes for tumors, while amplified fragments may harbor oncogenes. The genomic signature of each cell line in the collated data contains somatic copy number variation for 21,878 gene loci.The CNA data is downloaded from Cell Model Passports: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE68379. It reports how cancer-driven alterations detected in 11,215 tumors and 29 different tissues (integrating multiple omics) correlate with responses to 265 compounds in 1,001 cancer cell lines. Cell lines are very similar to tumors in these areas of alteration, and there are many examples of altered genes and pathways conferring drug sensitivity and resistance. Methylation is an important modification of proteins and nucleic acids that regulates the expression and shutdown of genes and is closely associated with many diseases such as cancer, aging, and Alzheimer’s disease, and is one of the key studies in epigenetics. Here we use DNA methylation, which turns off the activity of certain genes, and altered DNA methylation status is prevalent in tumors. The genomic signature of each cell line in our experiments contains the methylation status values of 365,860 CpG loci.The methylation data is downloaded from Gene Expression Omnibus (GEO): viau is a s represents the number of genes retained by the model, Hui Zou et al. first proposed the concept of sparse principal component analysis (SPCA) in 2006 . Suppose problem . Therefos-sparse projection operator. It is a s absolute elements of The following alternate iterative projection strategy  is used Our proposed model uses SPCA for dimensionality reduction and feature selection. SPCA is an unsupervised model, and a feature importance parameter Suppose there are Suppose The importance Finally, all the obtained importance scores are normalized to calculate the feature importance:M largest elements of the absolute value of The improvement process of the SPCA with feature importance is described as below. Firstly, the SPCA analyzes the data matrix After completing the SPCA with feature importance, we obtain the independent feature matrix for each omics data, the RNA-Sequence matrix Two main similarity calculation algorithms are used. The Pearson correlation coefficient is suitable for linear continuous variables and the Kendall correlation coefficient is suitable for discrete variables.For the RNA-Sequence and Methylation data, we use the Pearson correlation coefficient:The CNA data are integer discrete representing the variance multiples, so we use the Kendall rank correlation coefficient:After the similarity calculation, three independent sample similarity matrices are obtained, We construct a simple 7-layer deep neural network model and put the training .This neural network contains three one-dimensional convolutional layers, each convolutional layer is followed by a max pooling layer, and a batch normalization layer added after the last convolutional layer. In addition, the first two fully connected layers use “relu” as the activation function while the third fully connected layer uses “softmax”. The cross-entropy loss function is used, which is a commonly measurement in dealing with classification problems.The deep learning autoencoder model , in whicThe 35 drugs we selected include 30 targeted drugs and 5 non-targeted chemotherapy drugs, with the targeted drugs divided into 14 FDA-approved drugs in clinical use and 16 FDA-unapproved drugs. The results are evaluated using the sensitivity, specificity, precision, accuracy and F1-score of the model as indicators. Finally, we use the metascape platform to perform enrichment analysis of targets retained by our proposed method NDSP during feature selection and analyze the association and biological significance of these targets with that drug and disease.t and repeating the loops of SPCA and RF, the genes in the selected PCs can strongly correlate with the sensitivity of the specific drug.In the data preprocessing step, we collected and classified the multi-omics samples (cell lines) into sensitive and non-sensitive classes based on the binarized IC50 values of each specific drug. The unsupervised SPCA in our proposed model NDSP is first used for dimensionality reduction and feature selection. At this time, the PCs based on the SPCA may not relate with the specific drug. Therefore, the supervised model Random Forest (RF) is combined to evaluate whether the genes in the selected PCs could better predict the sensitivity of the specific drug. The feature importance parameter The mean values of each metric for our proposed method NDSP and the seven baseline models in the 30 targeted drug trials are shown in As can be seen from As shown in To verify whether our proposed model NDSP can work in precision oncology beyond targeted therapy, we conduct experiments on 5 non-specific therapeutic drugs. The mean values of each index for the eight models in the five experiments with non-targeted drugs are shown in The comparison results in To further validate the biological interpretability of our proposed model NDSP, we perform a biological enrichment analysis using the results of the multi-omics gene selection of the new model in Alectinib drug. The first principal component is obtained from the data of each omics in a SPCA module with the addition of a classifier. Drug Alectinib is mainly used for the treatment of non-small cell lung cancer and blocks the activity of ALK. The results of the pathway enrichment analysis are shown in A more concentrated distribution of gene sites selected by our model would indicate that the gene set is associated with a specific function or phenotype and is able to select pathways and gene sites that are more relevant to lung cancer. For example, in the RNA-seq omics results, the ERAD pathway corresponding to GO: 1904292 is highly associated with heritable lung disease regulatory mechanisms. And analysis in the integrated platform for integrating information on human disease-associated genes and variants (DisGeNET) shows that the selected gene sites are associated with non-small cell lung cancer, as shown in We proposed a novel drug sensitivity prediction model (NDSP) that combines biological multi-omics data, SPCA with classical machine learning classifier, patient similarity networks and deep learning. We use data from three omics: RNA sequencing data, Copy Number Aberration data and DNA methylation data. The SPCA with feature importance method is used for feature selection. Then we use patient similarity network to measure the similarity of the three omics feature matrices separately to obtain three matrices of We have conducted experiments using both targeted and non-targeted drugs. The available results show that our proposed model NDSP outperforms classical machine learning and deep neural network models in terms of sensitivity, specificity, accuracy, precision and F1-score. More importantly, the drugs selected for the experiments include both targeted and non-specific therapeutic drugs, which implies that the model has a certain degree of generality, and can be useful in precision therapy beyond traditional precision oncology and targeted therapy. The results of the enrichment analysis also show that the targets selected by NDSP are biologically interpretable and have some correlation with the corresponding drugs and diseases. This will guide physicians in selecting optimal treatment options while minimizing the negative effects associated with ineffective treatments, thereby fulfilling the promise of precision therapy.


The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially the ones based on deep-learning approaches, to improve diagnosis. Due to the high-dimensional small-sample nature of omics data, current deep-learning models end up with many parameters and have to be fitted with a limited training set. Furthermore, interactions between molecular entities inside an omics profile are not patient specific but are the same for all patients.In this article, we propose AttOmics, a new deep-learning architecture based on the self-attention mechanism. First, we decompose each omics profile into a set of groups, where each group contains related features. Then, by applying the self-attention mechanism to the set of groups, we can capture the different interactions specific to a patient. The results of different experiments carried out in this article show that our model can accurately predict the phenotype of a patient with fewer parameters than deep neural networks. Visualizing the attention maps can provide new insights into the essential groups for a particular phenotype.https://forge.ibisc.univ-evry.fr/abeaude/AttOmics. TCGA data can be downloaded from the Genomic Data Commons Data Portal.The code and data are available at  The disruption of different biological processes (BPs) can negatively affect an organism and lead to a disease state. Early diagnosis plays an important role in precision medicine in order to improve clinical decision-making. The development of high-throughput methods influenced precision medicine by enabling easy access to a large amount of biological information for each patient, known as omics profile. Omics profiles are high-dimensional complex signatures resulting from interactions of many molecular entities. The first common step in most machine learning approaches used in precision medicine is a feature selection procedure that reduces the data’s size to construct a classifier from a single-omics . As the Following the recent successes of deep learning in computer vision or natural language processing , differeCellular functions are governed by the combined action of multiple molecular entities which are specific to a patient. The expression of one gene may impact the expression of other genes differently in different patients. With classical deep learning approaches, these interactions which are learned during training, are assumed to be identical for all patients in the inference phase. It would be more beneficial to compute feature interactions that are specific to each patient. Self-attention can be used to improve the representation of the features vector by incorporating dynamically computed relationships between elements of the vector. It has been shown that the transformer architecture’s promising results extensively rely on attention mechanisms .Here, we propose a new method based on the self-attention mechanism to capture interactions between different molecular entities in order to predict the phenotype of patients, e.g. cancer types or the risk of death from omics data. Using a self-attention mechanism allows the model to capture feature interactions specific to each patient dynamically. Applying self-attention on high dimensional vectors such as omics profiles is challenging as the self-attention memory requirements scale quadratically with the number of elements. To overcome this problem, we propose to consider groups of features and apply self-attention to these groups. The architecture was tested on three different omics data: gene (mRNA) expression, methylation (DNAm), and micro-RNA (miRNA) expression and was compared with state-of-the-art deep learning-based methods. The results show that our proposed architecture better considers feature interactions in omics data and improves the model performance.Different deep learning approaches have already been tested on omics data: CNN, GNN, MLP, AE, and variational autoencoder (VAE). A VAE unsupervised training has been used as a pre-training for an MLP classifier , and thePromising results of CNN in computer vision inspired its application in precision medicine. Different strategies were developed to create a 2D image from an expression vector. Some approaches reshaped a 1D omics vector into a 2D image to exploit the capacity of CNN architectures to extract relevant visual patterns . Ma and In Deep learning architectures using attention mechanisms have been little explored for the application of omics data. Some approaches helped the network to focus on relevant genes for the predicted phenotype by computing feature importance scores with a small neural network . Those mIn our approach, AttOmics, we propose to embed groups of features in a lower dimension by considering all interactions inside this group and a new way of applying self-attention  to omicsn blocks used to construct a new representation of the inputs. Each block is formed of a grouped fully connected network (gFCN) module where each group is projected into a lower dimension with a fully connected network (FCN). Segregating features in groups restrict the potential interactions between features to the ones inside the same group. Multihead self-attention (MHSA) is applied to the set of groups to recover all possible interactions between groups. Around the self-attention block, a residual connection is added before applying a normalization. The encoder output is transmitted to an FCN used as the predictor.The model includes a grouping module and an encoder followed by a predictor, illustrated in p is the number of features and Y is the associated label. The training example X is split into groups according to a grouping strategy (see Section 3.2), k is the number of groups. For each group s-dimensional space with an FCN, a succession of fully connected layers (FCL) (Let rs (FCL) .Each FCL is the composition of an affine transformation of its inputs with a rectified linear unit  of interest, we restrict ourselves to terms available in GO slims.Inside the BP slim ontology, a gene can belong to more than one group; on average, they belong to two groups. Before applying self-attention, each group must be projected into the same dimensional space. Each group is projected with a different number of layers to have the same reduction ratio across different groups. This grouping strategy can only be applied to mRNA data.In the MSigDB hallmarks collection , there aK-Means or hierarchical clustering, can return sets of highly unbalanced clusters that may negatively affect the efficiency of our model. Large groups would require many parameters to be projected into a space with a dimension lower than the smallest group. Group unbalances would also imply a high compression of larger groups and almost no compression for the smallest group. To prevent this, we used constrained K-means clustering to ensure comparable group sizes  of class θ the model parameters.For classification problems, our model is trained end-to-end with a weighted cross-entropy loss to account for class imbalance:where iδ specifies if the event occurred for patient i, iT represents the time associated to the event and i. iT.For survival analysis, our model is end-to-end trained with a partial log-likelihood loss, as proposed in DeepSurv :where δhttps://portal.gdc.cancer.gov/). FFPE samples and bad replicates were removed according to TCGA consortium recommendation. Methylation data was restricted to the probes common to both HumanMethylation27 and HumanMethylation450 platforms. No feature selection was applied, and data were standardized to a zero mean and unit variance.TCGA data were used to evaluate our proposed approach AttOmics. We collected DNA methylation, gene expression, and miRNA expression data for 8416 patients of 19 different cancers and 361 normal samples from the GDC Data Portal , we ordered features based on their position on the genome, then used a 1D convolution, followed by a database  and was The hyperparameters of each approach are tuned on each omics data with a random search to achieve the best performances. The different values tested for each parameter are defined in the AttOmics is trained end-to-end using the Adam optimizer with a learning rate of 0.0001 and a batch size of 512. The maximum number of epochs was set to 100. An early stopping strategy is deployed to avoid over-fitting with a patience of 8 and a delta of 0.001 on the validation metric between two epochs.η, are concordant with their actual survival times.For the classification task, models were evaluated with the error rate. Prognosis prediction is evaluated with the concordance index . It estiA We investigate the impact of the main hyperparameters on the model error rate by applying a random search procedure. For each hyper-parameter, a random value is drawn from a set of predefined possible values. A model is trained with selected hyperparameters on the training dataset. For each grouping strategy, 1500 models are trained. The performance metrics reported here are estimated on the validation set. The results for the main hyperparameters of this experiment are presented in The encoder’s residual connection and the choice of normalization type greatly impact the performances. Adding a residual connection in an encoder block significantly impacts the model’s performance. It stabilizes the model performances . We explAnother important hyperparameter of the architecture is the number of groups . An incrThe selected hyperparameters for each grouping strategy and each omics data are presented in the Despite the broad adoption of high throughput methods in personalized medicine, the availability of omics data from cancer patients remains limited. We explore the impact of the training database size on the performances of AttOmics and other deep-learning architectures by training the different models on a subset of the training set. The different subsets are created by randomly sampling 10%, 30%, 50%, and 70% of the training set while preserving class proportions. To prevent data leakage, structures computed from the training set, like CoExp graphs or clustered grouping, are recomputed with the selected subset. For each subset, five models are trained. The reported performance metrics are estimated on the test set.P-values are corrected for multiple testing with a two-stage approach describe in CNN1d is the worst model across omics when trained on the whole training set. Convolutions are more suited for structured data, and even the incorporation of a structure in the data based on the chromosomal location is a constraint that limits the range of possible interactions. Only genes in the same convolution window interact with each other, which does not consider long-range interaction. The AttOmics model achieves better or similar performances than the best model that does not use self-attention across the different omics. For methylation data, the mean error rate is better than the GNN—CoExp approach. However, there is no statistical significance as the performances on the GNN are more variable across training. For gene expression data, similar error rates are achieved between MLP and GNN approaches.Depending on the omics, non-deep-learning models do not perform equally. The SVM approach obtains similar performances for the methylation data to AttOmics, whereas XGBoost and RF achieve lower performances than the CNN1d model. XGBoost and AttOmics have the same performances for gene expression data. SVM and RF do not compete with other architectures and obtain one of the highest error rate. For the miRNA data, the best-performing methods are RF and XGBoost. The SVM approach obtains an error rate comparable to the deep-learning architecture, and there are no differences between the different deep-learning models .We do not observe a difference in terms of performances between the different grouping strategies. For methylation data, random or clustering approaches give the same error rate. For gene expression data, GO and random grouping reach the same error rate, whereas the clustering approach has worse performances but still performs better than other deep learning approaches. The architecture based on the hallmarks grouping achieves performances similar to the MLP or GNN, whereas other grouping approaches improved the performance. The worst performance is probably due to this strategy’s implicit feature selection; only 4305 genes were used. A too-large selection of the number of features limits the potential for the model to learn the relevant interactions.Since, in real-world applications, datasets are much smaller than the TCGA dataset, it is particularly interesting to analyse the performances of models trained from small training sets. Reducing the number of training examples affects model performance adversely, as a limited training database hinders the capacity of the model to extract hidden information during training. The performances vary similarly between the different grouping strategies when reducing the number of parameters. When training with the lowest number of examples, we can identify different sets of architectures. For mRNA, the MLP has the worst performance. All architectures incorporating a structure in the data (CNN and GNN) achieve similar performances. AttOmics architecture performs best and outperforms non-deep-learning methods with small training sets for gene expression and methylation data.Only the hallmarks grouping strategy has worse performances than the other grouping strategies. We note that for the GNN architecture, the performances standard deviation across different training with the same number of training varies more than AttOmics architecture. This suggests that the GNN architecture depends on the selected training examples, whereas AttOmics models are less sensitive to this issue.Other classification metrics were improved, such as the f1-score . The impThe size of a neural network, i.e. its number of parameters to fit, has a strong impact on its performances and required hardware resources . AttOmics architecture reduces the number of parameters compared to the CNN architecture or MLP and achieves similar or better performances . Due to As self-attention is known to have a quadratic complexity, we explored its impact on the memory usage of the model and the runtime to obtain prediction from the test set . During A similar study is performed for prognosis prediction. The evolution of the To conclude these results, incorporating self-attention in the architecture allows having a softer compression of the features, improving the representation of the data and, therefore, the performances.AttOmics works on methylation data (DNAm) and gene expression data (mRNA) but not on miRNA expression data. For miRNA data, the best performances were obtained with non-deep-learning approaches. There is some uncertainty when using biologically motivated groups, as the domain knowledge constantly evolves. When using biologically aware groups, the model is limited to only considering a subset of the possible feature interactions. Random or clustering grouping can detect relevant interactions not yet included in the biological knowledge. Future work will investigate different biological knowledge. Nevertheless, in this article, one can prefer biologically motivated groups that are more interpretable, although the other grouping strategies give slightly better results. This reflects the well-known trade-off between performance and interpretability in machine learning .One advantage of using self-attention in the model is the ability to visualize the learned interactions . The attThe self-attention mechanism used in the architecture was not designed to be an interpretability tool. However, it can provide information on the interaction learned by the model depending on the grouping strategy. We used GO slim or MSigDB hallmarks as a biological knowledge-aware grouping strategy. The terms used in GO slim represent general BPs; the high level of the terms makes it difficult to link the detected interaction with the phenotype. On the contrary, MSigDB hallmarks focuses on important and specific BPs and are, therefore, easier to interpret.With the hallmarks grouping, we identified interactions between well-known pathways in cervical cancer: Wnt signaling, HedgeHog signaling, and JAK/STAT signaling . STAT prInterestingly, we identified similar interactions learned by the model between the GO slim and hallmarks grouping strategies. For BLCA cancer, a model based on GO slim identified multiple interactions involved in the inflammatory response (GO : 0006954). In the model based on hallmarks, the inflammatory response group is also identified as involved in different interactions . The groThe model can handle any grouping, handcrafted groups, or based on a different knowledge source that could be used to improve the information contained in an attention map.In this article, we propose AttOmics, a novel deep-learning architecture for personalized medicine. AttOmics leverages self-attention to capture feature interactions specific to each patient. Features were grouped before applying self-attention on high dimensional vectors, such as omics profile. With this approach, we can reduce the number of parameters compared to an MLP with a similar dimension while accurately predicting the type of cancer. The self-attention also allows the visualization of the learned interactions to understand the model better. AttOmics is the only architecture consistently performing well on different omics data.In future works, we will explore the use of linear approximation of self-attention maps for high-dimensional vectors. For example, to reduce the memory footprint of self-attention, new algorithms have been proposed to compute self-attention sequentially on chunks of queries . DiffereIn this study, the different omics profiles were studied individually. Considering the different omics profiles in a unique model could improve the prediction performances by exploiting the complementary information between the different omics profiles. The attention mechanism has proven to combine multiple modalities within a joint representation effectively. In our subsequent work, we will use attention-based multimodal deep-learning models to integrate the different omics data into the same model. Therefore a fusion of the different omics profiles with the attention mechanism could be computed directly onto the hidden layers of the model.btad232_Supplementary_DataClick here for additional data file.