This enables exploration of multi-modal datasets arising from profiling of patients with different severities of illness admitted to hospital in the first phase of the pandemic in the UK prior to vaccination, compared with community cases, healthy controls, and patients with all-cause sepsis and influenza. These data include whole blood transcriptomics, plasma proteomics, epigenomics, single-cell multi-omics, immune repertoire sequencing, flow and mass cytometry, and cohort metadata. COMBATdb provides access to the processed data in a well-defined framework of samples, cell types and genes/proteins that allows exploration across the assayed modalities, with functionality including browse, search, download, calculation and visualisation via shiny apps. This advances the ability of users to leverage COMBAT datasets to understand the pathogenesis of COVID-19, and the nature of specific and shared features with other infectious diseases.Advances in our understanding of the nature of the immune response to SARS-CoV-2 infection, and how this varies within and between individuals, is important in efforts to develop targeted therapies and precision medicine approaches. Here we present a database for the COvid-19 Multi-omics Blood ATlas (COMBAT) project, COMBATdb ( We provide two advanced ways of exploring the samples on the sample details page. The first shows the breakdown of sample attributes (for example cohort) that have been stored in the database. The second displays all samples that possess the specific attribute. COMBATdb supports the browsing of key processed data from the major modalities for participants, participant timepoints and samples, allowing the users to interrogate the various types of data from the same sample, participant timepoint or participant. For example, for each participant, participant time point and sample users can rapidly have an overview of which of the 13 individual modalities are available and browse them Figure . In addiThe ‘Search’ function supports search by metadata in three modes: single, multiple and customisable. For single and multiple mode, the search results will return detailed information about single or multiple terms from modalities, participants, participant timepoints, samples, sources, genes and cell types. Using the customisable mode generates a list of samples, participant timepoints or participants that are defined by a given modality and a given source. For multiomics data, ‘single mode - multiomics data’ searches for a single participant, participant timepoint, sample, gene or cell type and returns the results of multiomics data on the same page, which enhances the comparison capability of cross modality data Figure . The ‘ceThe ‘Primary’ function offers a way to access the data from each modality with different layouts for different modalities of data Figure . For genThe ‘Compare’ function provides pre-calculated differential abundance analysis tables across two comparator groups of samples under the cut-offs of FDR < 0.05 and fold change > 1.5 for 7 out of 8 modalities. More stringent cut-offs (FDR < 0.01 and fold change > 2) have been applied for the ‘CITE-Seq: GEX Pseudobulk’ modality. For some of the modalities, this function offers functional analysis using Gene Ontology ,33 termsThe ‘Integrative’ function provides a high-level picture of multimodal data analysis integration. The ‘Tensor decomposition’ module offers two ways of starting to query the data, through either feature or SDA component. Each component comprises vectors of scores (loadings) indicating the contribution of individual features  linked by that component Figure . This deThe ‘Download’ function provides two tables that contain information such as dataset ID, description and accession number as well as links to download the key processed datasets for this database and the COMBAT project more broadly, and how to access raw datasets.We have prepared shiny apps (using the R shiny package) for a selection of modalities to allow differential abundance analysis and data visualisation.The differential abundance analysis module accepts the user-provided options of two comparator groups (case versus control), sample inclusion strategy, covariates, FDR threshold and fold change threshold. The results are in the form of tables, volcano plots and boxplots. We provide three different strategies for inclusion, namely ‘all samples’, ‘priority samples’ (the first sample available at the maximum sampled severity for each patient) and ‘priority samples at maximum severity’  to give user flexibility in the analysis. Covariates such as ‘age and sex’, ‘age’ and ‘none’ are adjusted for in the differential abundance analysis by defining the appropriate design matrix to fit the linear model in edgeR package  or limmaFor the data visualisation module, we adopted high dimensional reduction techniques to generate principal component analysis (PCA) plots, correlation plots, loadings plots and heatmaps based on the user defined dimension for X axis, dimension for Y axis, sample inclusion strategy and covariates. Only source groups that contain at least 3 samples have been retained for further analysis and are displayed in the plots. For ‘Flow Cytometry: FACS’ and ‘Mass Cytometry: CyTOF ’, we use the CATALYST package  to prepaIn anticipation of further data and modalities becoming available as part of COMBAT using existing or new samples, we adopted a modular and scalable philosophy in designing and developing COMBATdb. For the database schema, we created a number of MySQL tables to contain the metadata and modality data in a highly efficient manner. In brief, we have separate tables for modality, participant, participant timepoint, sample, source, gene and cell type. The key linker table is the many-to-many ‘modality2sample’ table. Each modality has an independent table for ‘primary’ and ‘compare’ function. To expedite data queries, indices have been created on certain columns of the data table. To mitigate against SQL injection security risks, we employed prepared statements throughout the platform. All the tables in the interface are searchable, sortable, and downloadable. To improve the performance of R shiny apps, we stored the input data in R object files that have been sufficiently processed and, in this way, the response time of running shiny apps is significantly reduced to an acceptable time period.2FC = 0.88), consistent with its role in innate immunity including as an inducible upstream modulator of TGFβ signalling    Figure  which ca) Figure . The dif) Figure  includin) Figure , with co) Figure , plasma ) Figure  and diff) Figure .This example highlights the way the COMBAT datasets can be explored within COMBATdb, and a particular example involving LRG1 which adds to evidence for its potential utility as a biomarker and therapeutic target in a variety of diseases .https://mlv.combat.ox.ac.uk/  and European Genome Phenome Archive (EGAS00001005493) and ProteomeXchange Consortium via the PRIDE partner repository (PXD023175) are provided within the source publication (Details of accession numbers for deposited data at Zenodo (gkac1019_Supplemental_FileClick here for additional data file.


Single Cell RNA Sequencing generates gene expression data at a single cell resolution. While single cell RNA has many applications in biomedical research, the high dimensionality of the data produced poses a considerable computational challenge. This study proposes a novel algorithm using penalized regression methods to analyze single cell RNA sequencing data. The proposed algorithm reduces high dimensionality of the gene expression data using a sequence feature selection methods such as Ridge regression, LASSO, Elastic Net, Drop LASSO, and Sparse Group LASSO. The proposed algorithm successfully detected highly differentiated genes, including the marker genes, for 5 different single cell RNA sequencing datasets associated with the species mouse, plant, and human.With the emergence of single-cell RNA sequencing (scRNA-seq) technology, scientists are able to examine gene expression at single-cell resolution. Analysis of scRNA-seq data has its own challenges, which stem from its high dimensionality. The method of machine learning comes with the potential of gene (feature) selection from the high-dimensional scRNA-seq data. Even though there exist multiple machine learning methods that appear to be suitable for feature selection, such as penalized regression, there is no rigorous comparison of their performances across data sets, where each poses its own challenges. Therefore, in this paper, we analyzed and compared multiple penalized regression methods for scRNA-seq data. Given the scRNA-seq data sets we analyzed, the results show that sparse group lasso (SGL) outperforms the other six methods  using the metrics area under the receiver operating curve (AUC) and computation time. Building on these findings, we proposed a new algorithm for feature selection using penalized regression methods. The proposed algorithm works by selecting a small subset of genes and applying SGL to select the differentially expressed genes in scRNA-seq data. By using hierarchical clustering to group genes, the proposed method bypasses the need for domain-specific knowledge for gene grouping information. In addition, the proposed algorithm provided consistently better AUC for the data sets used. Single-cell RNA sequencing (scRNA-seq) technology is very popular in biomedical research. This technology examines the gene expressions among different cells in a tissue and provides the gene expression profile on a cell level . ComparescRNA-seq technology is also useful for identifying cellular heterogeneity. On the other hand, scRNA-seq has low capture efficiency and high dropouts compared with regular RNA-seq technologies. Yet, quality control is necessary for removing technical noise from scRNA-seq data . scRNA-squencing . In addiprotocol . UMI tagprotocol . UMI tagprotocol . Non-UMIprotocol .One of the many applications of scRNA-seq technology is differentiating cell groups by comparing their molecular signatures, for instance, identifying highly differentiated genes to cluster knockout and wild type cells. Similarly, by comparing the gene expression profile of cancer cells with that of healthy cells, one can identify genes with altered expression that might be responsible for cancer. The applications of scRNA-seq technology are numerous and of high impact in genomic research. However, the scRNA-seq data come with certain challenges as well , especiap as the dimension of features and n as the dimension of samples. When scRNA-seq data usually contain a larger number of features (genes) than the number of samples (cells). People typically represent features . There aFeature selection is the method of selecting variables that contain better signal than the noise variables for the target variable of interest Random forests, Recursive Feature Elimination (RFE), and penalized regression are often used for feature selection in ML.In this research, we explored the penalized regression methods to select feature variables over RFE and random forest. The rationale behind this choice is that RFE is computationally expensive when applied to high-dimensional data . TherefoSome popular variants of penalized regression in machine learning are ridge regression, least absolute shrinkage and selection operator (lasso) , and elaThere are some other variants of lasso regression such as nuisance penalized regression , fused lThere exist studies in penalized regression methods for high-dimensional data ,25. HoweThe rest of this article is organized as follows. To reach our objectives, we used several penalized regression methods in this paper. These regression methods and the performance metrics suitable for this study are described below.Y is a n-vector of response variable, p-vector for the regression coefficients, and Y are centered and scaled in Equation . RegresThe Least Absolute Shrinkage and Selection Operator (lasso) regression  minimizeElastic net regression, proposed by , is a meThe L1 norm part of the penalty generates a sparse model and the L2 norm part of the penalty encourages greater shrinkage to large coefficients .n be the number of observations. For each group j, let j and Yuan and Lin  proposedHere, the vector of regression coefficients is One possible limitation of group lasso is that while it selects a collection of subsets of features, all of the estimated regression coefficients in a selected subset of features will be nonzero. That means there is no variable selection within a selected subset or group of features. Often, both sparsity of subsets and features within each subset might be important in an application. In scRNA-seq application, identifying important genes in the biological pathways may be of interest. Simon et al.  proposedn observations and p predictor variables. After that, each of the permuted rows in p to create a new matrix The modeling of scRNA-seq data may become complicated and challenging to create a number of modeling and computational problems. For example, as there is a little amount of RNA is present in each cell, a big proportion of polyadenylated RNA might be stochastically lost during sample collection including cell lysis, reverse transcription, or amplification. As a result, a good collection of genes may fail to be detected even when they are expressed, introducing a type of error referred to as dropouts. This may introduce a lot of zeros in the scRNA-seq data. The presence of excess zeros in the data might pose considerable challenges to the analysis and biological interpretation, and may give rise to new stochastic models for data shifting and scaling, and visualization or gene differential analysis. As a remedy, Khalfaoui and Vert  proposedZeng and Breheny  implemenK-Means clustering. They are briefly described below.Clustering is the process of grouping data instances into clusters so that the instances in the same cluster have high similarity . Two popIn hierarchical clustering, the data values are placed into groups or clusters hierarchically . The resK-Means clustering algorithm also clusters data values into groups. The K-Means algorithm starts by randomly selecting k centroids of p-dimensional feature space [k are often supplied by the user. After that, the clusters are formed by repeatedly assigning data values into their nearest centroid. The algorithm continues iteratively by recalculating the centroids in such a way that the within cluster sum of squared error is minimized. Often, regularization is performed to determine the optimal number for k, which may improve prediction accuracy [As in hierarchical clustering, the re space . The iniaccuracy . In thisk-fold CV, the data values are first randomly divided into k folds, of which k random folds separately by swapping the folds for a total of k times. The average prediction performance from the k test folds is used as the overall prediction performance of the model. In this study, we used 10-fold CV by assigning Cross-validation (CV) is a method of randomly dividing data values into folds, which we often call training and test folds, to calculate prediction performances of the models and to tune model parameters . In k-foTo evaluate the performance, the methods are compared using the performance metrics calculated from a confusion matrix  shown inSensitivity, also known as recall or True Positive Rate (TPR), is the proportion of positive cases that are predicted positive out of all positive cases. This metric is useful to detect positive cases when the classes are imbalanced .SensitiSpecificity is the proportion of negative cases that were predicted as negative out of all negative cases. False Positive Rate (FPR), calculated asced data .Specifiy-axis and the false positive rate (FPR) on the x-axis [An ROC is a two-dimensional plot that assigns the true positive rate (TPR) on the e x-axis . Often, e x-axis .K-Means clustering. The steps to implement the proposed algorithm are presented in Algorithm 1.Algorithm 1: Steps to implement the proposed algorithm1.Load data set into R and assign classes 1 and 0 to the two selected group of cells to form a binary classification problem.2.Shuffle cells within each class to randomize the data points.3.Remove genes with no variability in expression across all cells.4.(a)Fit ridge, lasso, elastic net, and drop lasso.(b)Find the top important genes from each method. The top genes are the genes that have coefficients above a cut off .(c)Form a gene pool by taking union of the top important genes from the 4 models; for instance, (d)Fit SGL with the new gene pool pre-grouped by hierarchical clustering.(e)Save the coefficients of SGL.(f)Repeat the steps for a 10-fold CV.Split the data set into training (90%) and test (10%) for 10-fold cross validation.5.Calculate the average of coefficients for each gene across the 10 folds and sort the genes.6.Visualize the gene versus coefficients plot and select the final set of genes using an elbow curve.7.Cluster all the cells by applying K-means clustering on the top important genes.In this paper, we propose a novel algorithm for feature selection using 5 penalized regression methods . The proposed algorithm aims to handle the ’large-p-small-n’ problem of scRNA-seq data through step-by-step feature selection, as shown in http://imlspenticton.uzh.ch:3838/conquer/ accessed on 1 July 2021), a curated database of continuously processed, analysis-ready, and well-documented scRNA-seq data. There are over 40 publicly available scRNA-seq data sets on this website, and they all have the count and transcripts per million (TPM) estimates for genes, quality control, and exploratory analysis reports. The remaining three datasets  are all available from the Gene Expression Omnibus (GEO) and can be retrieved using the accession numbers. We summarize these data sets in There are a total of 5 data sets used in this study. To compare the penalized regression methods, 4 scRNA-seq data sets from 3 different species  were used. One additional data set was used to compare the performance of the proposed method with the best-performing single method (SGL). This study used real data sets rather than simulated data sets because real data sets often contain variables with complex interrelationships that are difficult to reproduce using a simulation. Two data sets were downloaded from Conquer  was selected for further analysis. This data set was generated from an experiment to investigate human lung epithelial cell subpopulation between healthy donors and idiopathic pulmonary fibrosis (IPF) patients . IPF is We started the analysis by preprocessing the data sets to make sure they were compatible for use in different R packages used. Data sets are available as a single CSV file or multiple files with genes as rows and cells in the columns. Multiple files were combined into a single file and transposed to obtain a single matrix with cells as rows and genes in the columns.In this research, we focused on binary classification. Therefore, two cell groups were created and labeled as classes 0 and 1 for each experimental data set. Cells were also shuffled within each class and for the data sets with significant differences in the numbers of samples for the two classes. Therefore, we balanced the data sets before conducting analysis. For all the data sets, genes with no expression variance across all cells were removed. We then verified how each method performs in terms of AUC, computational time, and cross-validation with the different scRNA-seq data used. Here, a 10-fold stratified cross-validation was performed to calculate the performance metrics. We used different hyperparameters for each method, which are shown in After calculating the performance metrics, the best-performing methods were selected and combined to form the proposed algorithm for feature selection using penalized regression methods. Finally, the performance metrics of the proposed algorithm were compared with those of the top-performing method. All the experiments are conducted on an Ubuntu 20.04.4 LTS (GNU/Linux 5.4.0-100-generic x86_64) virtual machine, hosted by Compute Canada, with 32 GB RAM. The programming language used is R Project for Statistical Computing version 4.1.2 .p-value = 15%. An Analysis of Variance (ANOVA) test such as the Friedman test alone cannot reveal which of these methods has a significant difference in performance [The first objective of this study is to compare the performance of the selected penalized regression methods. The results in terms of cross-validated AUC (CV-AUC) and computational time are shown in formance . Therefoformance . A NemenAfter comparing the computation time of the methods given in The second objective of this study was to combine the top-performing penalized regression methods to improve predictive AUC and perform gene selection. From the discussion of the results of the first objective, we observed that SGL and grplasso were better candidates to form a new method. In terms of gene selection, SGL outperforms grplasso. SGL could identify the top important genes in one fold of 10-fold CV, whereas grplasso takes multiple folds to obtain the same result. In other words, the results of top important genes changed from fold to fold of the 10-fold CV for group lasso. Therefore, SGL was chosen over grplasso for the proposed algorithm. SGL achieves better AUC than biglasso in comparable time for data sets of size 63 MB to 252 MB when tested on a computer with a 32 GB processor. Since biglasso did not show a significant improvement in computation time relative to SGL, biglasso is not included in the proposed method.To develop the proposed algorithm using penalized regressions, we selected the ridge, lasso, elastic net, and droplasso to form a filter that produces a gene pool with the number of genes reduced considerably relative to all the available genes. The gene pool was formed by taking a union of the top important genes selected from the four methods as the top important genes have some variations between methods. A union of top important genes is, therefore, more likely to capture the important and differentially expressed genes. The gene pool thus formed is grouped and sent down to SGL to calculate predictive AUC.The experimental data used to test the performance of the proposed method include all four data sets in the benchmarking, and also a new independent data set (GSE157997). This data set has the largest number of cells among all five data sets, and was not used to develop the proposed algorithm. It is therefore expected to generate a fair and independent comparison between the proposed method and others.In the proposed algorithm for feature selection using penalized regression methods, feature selection is performed in two stages. The first stage creates a gene pool that has a significantly reduced number of genes (less than 50%) compared to the original data as shown in K-Means clustering.From our experiments, the hierarchical clustering and SGL originally required a 32 GB RAM processor for the computation. With the contribution of the proposed method, large numbers of genes are reduced in the gene pool. We notice that the proposed method can be executed on an 8 GB RAM processor for the following data sets: GSEGSE60749 and GSE71585. The proposed algorithm can be used for other high-dimensional data sets and feature selection as well. As shown in Essentially, the proposed algorithm tackles the “large-p-small-n” problem by applying feature selection, on the high-dimensional scRNA-seq data, in a sequential fashion. At each stage, different penalized regression methods are used for feature selection. The first stage  reduces the number of genes to less than 50% of the original size, and the second stage (SGL) reduces it further to make the number of genes comparable to the number of cells.In this section, we discuss the final subset of genes selected by the proposed method and the cell clustering within each data set to reveal potential biological interpretation and meanings.The genes versus coefficient plot for the second data set GSE71585 is shown in The proposed algorithm for feature selection can cluster the primary visual cortex cell groups well  with theThe most important genes for the data set GSE123818 found by the proposed method are AT2G43610, AT4G05320, AT2G07698, and AT3G51750 . This daThe set of selected genes by the proposed method in the GSE81861 data set are shown in the genes versus coefficients plot . For GSELooking at The last data set GSE157997 is used to independently evaluate the performance of the proposed algorithm with others. In terms of AUC, the proposed algorithm performs the same as the best-performing single method (SGL) . The proThe penalized regression methods select genes that are highly different in their expression between cell types. In our study, the penalized regression methods were evaluated for their ability to predict cell types based on the genes differentially expressed between them. Then, we proposed an algorithm using these penalized regression methods to select genes through successive stages of feature selection to improve cell type prediction performance via AUC. We have shown that, for all data sets, some of the top genes from the subset of differentially expressed genes selected by the proposed algorithm are known marker genes. We suggest using the genes identified by our proposed method as a pool of candidates for the future wet-lab experiments for verification.This study benchmarks several state-of-the-art penalized regression methods on their performance to scRNA-seq classification. Based on the findings of the benchmarking, we proposed an algorithm of penalized regressions, which improved the prediction performance.The results and findings show that the SGL method outperforms other methods in terms of predictive AUC and computation time. We note that the penalized regression methods can have many hyperparameters, and changes in these hyperparameters may affect the results. For instance, the number of groups of genes and the methods used for grouping the genes can cause differences in predictive AUC, computation time, and final gene selection by group lasso and SGL. The proposed algorithm for feature selection shows a better prediction compared with SGL.The advantages of our proposed method are two-fold in the sense that it uses hierarchical clustering to find the grouping information of genes that bypasses the need to have much knowledge of genes in scRNA-seq data prior to the execution of SGL, and yet the differentially expressed genes selected by the proposed method and the cell clusters in the data have a strong association. It is because the proposed algorithm for feature selection carries out the feature selection by creating a gene pool based on the union of the top genes from four different methods. This step ensures that any relevant genes that may have been missed by any one of the penalized regression methods are highly likely to be captured by other methods and subsequently included in the gene pool. Due to the sequential nature of analysis and two stages of feature selection, the proposed algorithm may require more time to execute than SGL alone. However, the proposed method uses a smaller subset of genes as input for SGL, thereby reducing computational memory requirements from 32 GB RAM to 8 GB RAM.This research has the potential to be extended to include more methods and relevant R packages, such as the Seagull , which a


The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connection with clinical cohort and ribonucleic acid (RNA) sequencing data, including single-cell ribonucleic acid (scRNA) sequencing data. This paper investigates statistical techniques and artificial intelligence (AI) based non-small cell lung cancer transcriptome data analysis methods divided into target and analysis technology groups. The methodologies of transcriptome data were schematically categorized so researchers can easily match analysis methods according to their goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. Transcriptome analysis methods are divided into three major categories: Statistical analysis, machine learning, and deep learning. Specific models and ensemble techniques typically used in NSCLC analysis are summarized in this paper, with the intent to lay a foundation for advanced research by converging and linking the various analysis methods available. According to data published in the Cancer Journal for Clinicians , the numIn this paper, various artificial intelligence (AI) based transcriptome analysis methods and predictive models are investigated to provide future guidelines for more effective NSCLC treatment development. NSCLC can be subdivided into squamous cell carcinoma, adenocarcinoma, and large cell carcinoma, among which squamous cell carcinoma and adenocarcinoma are most common. Mutations are another important element in the study of NSCLC and being able to accurately classify lung cancer types is essential in selecting treatment options and identifying oncologic mechanisms. This is because the targeted therapy or chemotherapy applied to lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients are different . In addiThese studies are based on next-generation sequencing (NGS) analysis . As reseIn recent years, research that enables direct clinical application by applying a cancer prognosis , metastaTo help the RNA-seqeuncing (RNA-seq) analysis process, this paper focuses on providing a guideline on various AI and mathematical and statistical methods that can be used in extracting effective RNA information and discovering biomarkers for NSCLC patients. Various RNA-seq analysis methods, such as gene expression, gene fusion, and mutation, have been applied in the past. In addition, gene set enrichment analysis (GSEA) and pathway analysis have been used in various medical fields to extract gene expressions. The accuracy of the prognosis or symptom to be predicted varies depending on the AI model used, and many approaches already exist, which are difficult to distinguish the pros and cons. Therefore, this paper investigates various RNA-seq analysis methods using AI technologies in hope to help future clinical research. For example, a binary prediction model that classifies and clusters a patient’s tumor mutation from raw data of the RNA-seq can be constructed, or a regression model that predicts whether a patient will recur or not can be designed.In this paper, RNA-seq analysis using AI technologies are divided into three categories, which are statistical analysis, machine learning, and deep learning. Statistical analysis is effective in finding out which gene groups have significant values. Statistical techniques help to visualize the analyzed result as a volcano plot or heatmap to identify the tendency. Various test techniques are included by applying existing statistical analysis methods used in other fields . The regTo properly use RNA-seq data, preprocessing is indispensable, as the range of values varies according to the type of RNA-seq and because gene expressions are different depending on the patient and patient group. The order of the preprocessing RNA-seq data method is slightly different depending on the nature of the RNA-seq data. First, in the case of bulk RNA-seq data, differentially expressed genes are derived through trimming, counting, and normalization. An alignment process may be added after trimming, which is an additional way to align data. Second, in the case of single-cell RNA-seq data, Quality Control and Normalization are performed, and the results are grouped through Clustering. After this, differential expressed genes were obtained in each group. For more details on the process, please refer to etc. In the predictive biomarker category, studies were also conducted to identify immune checkpoint inhibitors (ICB)   or to idrs (ICB) . Beyond rs (ICB) . There ars (ICB)  or identrs (ICB) . Analysirs (ICB)  and progrs (ICB) , which prs (ICB)  can be crs (ICB)  that clars (ICB) .etc., is also needed. In general, classifying cancer subtypes of NSCLC is treated as a multi-group classification problem because there are more than three groups to be distinguished , reads per kilobase per millions mapped reads (RPKM), and transcripts per million (TPM). However, if the extracted RNA-seq data does not show a significant difference in scale, the raw counts of RNA data itself can be directly used.p-value and the fold change (FC) value. DEG analysis can be performed with the Kaplan-Meier test using the log-rank test, or DEG analysis can be performed through gene pathway analysis. As shown in This procedure is a preparation step for analysis using FASTQ from raw RNA-seq data. FASTQ is a text-based format to store all quality (Phred) scores for each nucleotide expressed in ASCII code in the adenine, guanine, cytosine, thymine (AGCT) biological sequence. It can be divided into a technique to quantify and analyze gene expression from RNA-seq raw data and a technique to analyze differential gene expressions. First, considering the gene expression quantification technique, it mainly proceeds to 1) Trimming, 2) Counting, and 3) Normalization. Trimming is the procedure of removing contaminated or low-quality data from raw data. It is an optional step, but the quality of the data increases when it is executed, and the final analysis accuracy increases. Differentially expressed genes (DEG) analysis is a process of extracting meaningful RNAs. For this purpose, various test techniques can be used based on the After completing the previous three steps of Until now, the transcriptome of a cell population has been studied, but the precise results of the study are limited because the expression patterns of each cell are different. Therefore, there is a limit to the analysis to understand the cancer microenvironment in which various types of cells exist only by bulk RNA-seq. A method for deconvolution of the cell type ratio was developed from the bulk RNA-seq results . But sinRNA-seq analysis methods can be classified based on the data type to be analyzed and the AI technique to be applied. Analysis methods using AI can be divided into three categories: statistical analysis, machine learning, and deep learning. First, statistical analysis methods can be divided into test technique, proportional hazard model, and regression. Test techniques can be subdivided into the Kaplan-Meier test  and log-p-value), where it is possible to cluster patients based on a major classification criterion. For example, regression is commonly used to divide the overall survival rate, disease-free survival rate, and median survival value into groups of patients who have undergone different treatment methods and determine the significance of the survival rate between the two groups.Medical practitioners who study cancer patients as well as NSCLC mainly use the survival model. It is a statistical analysis method that estimates the survival time from the start of treatment to death of a patient . HoweverP(k) can be calculated based on the ratio of the number of survivors in each interval. For example, if one person dies during the observation period, the interval survival rate is (n − 1)/n, where n is the number of patients under observation. Finally, the cumulative survival rate S(k) required for the Kalman-Meier test can be obtained by sequentially multiplying the interval survival rate according to Eq. Ns is the number of survivors up to period k and No represents the number of observations up to period kIn , the Kape is n − /n, wherep-value less than 0.05) is used to compare the survival rates according to the disease-free survival period in  of lung cancer patients exists. In addition, the Cox-ph model utilizes the hazard ratio (HR), where it assumes that the HR is always constant. The Cox-ph model uses survival functions like Kaplan-Meier, where a survival function expressed by S(t) represents 1 at first, and S(t) would converge to 0 when infinite time has elapsed. Using a NSCLC patient as an example, the Cox-ph model will have S(t) = 1 at the starting point of observation. Through the survival function, it is possible to derive the lifetime distribution function F(t), which represents the probability that the observed event will occur within a specific time. By differentiating the lifetime distribution function, it is possible to obtain the survival density function f(t), which represents the event rate at a specific time. The relation of S(t), F(t), and f(t) is expressed in Eq. The Cox-ph model is the best-known method for screening prognostic variables that have a significant effect on the survival rate of lung cancer. In particular, the Cox-ph model is commonly used to compare patients with any RNA factor in two patient groups. For example, a group of patients with a high and low TGFB1 ratio has been analyzed as a survival fraction over time in . A new bH(t) uses the probability that an event will occur at an arbitrary point in time h(t). The hazard function is based on a conditional probability that represents the probability that an event will occur for a case in which the event has not occurred until a specific time. That is, the hazard function H(t) uses the probability relation of h(t) = f(t)/s(t) and is defined in Eq. The hazard function In the analysis of RNA sequencing of NSCLC using machine learning, supervised learning techniques represented by SVM classify subtypes of cancer, and regression models represented by LR are used for survival analysis. Machine learning is used to assist decision-making in clinical studies of various cancer types (including lung cancer) and can be used with a combination of various other analysis techniques . NSCLC rMachine learning techniques do not stand alone. Machine learning techniques use a combination of two or more techniques depending on their purpose. In , genes wDeep learning based RNA-seq analysis has been in the spotlight recently. Deep learning in the medical field has been mostly used for pattern recognition and medical image processing. For example, various imaging methods have been introduced to detect and analyze cell tissues . This isClassification or prognostic prediction using the machine learning techniques described above have meaning only in the final stage analysis. Thus, research on combining the characteristics of deep neural networks (DNNs) with regression models was attempted, and an extension of the Cox-regression model using a DNN was proposed in , which wHθ(x) parameter based on the DNN weight θ (DeepSurv is a multilayer perceptron model which consists of hidden layers consisting of fully-connected non-linear activation functions similar to the Faraggi-simon network consisting of a single hidden layer with two or three nodes . DeepSurweight θ . DeepSurDCNet is an autoencoder-based deep learning model that predicts about 400 cell types from a bulk RNA-seq dataset and discovers marker genes . As presSelection of a RNA-seq method to apply to NSCLC data depends on the target of the analysis type, which may not be easy to select. To assist this process, in this paper, various analysis methods depending on the objective of RNA-seq are summarized in This paper investigates various AI analysis methods of RNA-seq research and provides guidance on how to apply this in NSCLC analysis and predictions. Although there are many papers on this area, there is no known single dominant method on how to apply systematic AI learning technologies in analyzing NSCLC RNA-seq data. The best way to obtain the most accurate analysis result is to select the research goal and the corresponding model properly, in which


The synovial fluid (SF) analysis involves a series of chemical and physical studies that allow opportune diagnosing of septic, inflammatory, non-inflammatory, and other pathologies in joints. Among the variety of analyses to be performed on the synovial fluid, the study of viscosity can help distinguish between these conditions, since this property is affected in pathological cases. The problem with viscosity measurement is that it usually requires a large sample volume, or the necessary instrumentation is bulky and expensive. This study compares the viscosity of normal synovial fluid samples with samples with infectious and inflammatory pathologies and classifies them using an ANN . For this purpose, a low-cost, portable QCR-based sensor (10 MHz) was used to measure the viscous responses of the samples by obtaining three parameters:  Synovial fluid (SF) is a viscous liquid located in the joints whose primary functions are twofold. The first is the joint’s mechanical function, which involves lubricating the articular surface and cushioning movements. The second one is to contribute to the nutrition of the articular cartilage by acting as a nutrient transport medium. It is composed of dialysate of plasma and a high content of hyaluronic acid (HA), which is responsible for its viscosity ,2.The analysis of SF begins with the extraction of the sample (by arthrocentesis), which involves a joint puncture. Then, the sample is collected in tubes containing anticoagulants such as EDTA (ethylenediaminetetraacetic acid) and lithium heparin ,3,4. RegTo determine the viscosity  as sensors to detect viscosity changes has been demonstrated ,12,13. TWithin the ViSQCT project of the Universidad Politécnica de Madrid (UPM), we developed a prototype sensor whose operation is based on the use of QCR. Its use in characterizing the viscosity of hydrogel formation has been previously demonstrated . In a prAs part of the development of the device, an Artificial Neural Network (ANN) was implemented to optimize the classification of SF samples. ANNs have made inroads in biomedical engineering thanks to their ability to find relationships between data for prediction or classification ,32. SomeIt is demonstrated that the ViSQCT sensor effectively measures the viscosity change in low-volume samples of SF.A complete methodology is proposed to differentiate between inflammatory and infectious SF.We show that using classification models such as ANN improves the methodology by increasing classification accuracy.We compare the performance of the methodology and the system when using SF samples stored in two types of tubes (tubes with EDTA and tubes with lithium heparin) and evaluate their influences on making an accurate differentiation.The main contributions of the paper can be summarized as follows:The present work shows the use of a portable and low-cost (less than EUR 200) QCR-based sensor named “ViSQCT” (developed in-house at the UPM) which allows the characterization of the viscosity of a small volume sample (few microliters) to classify between inflammatory and septic SF. The ethics committees of both the hospital and the university approved this work.The Hospital Universitario La Paz  provided the SF samples. We used the remnants of samples sent to the Emergency Laboratory collected from July 2021 to September 2021 to be analyzed for diagnosis. Thirty-three samples from different patients were provided in tubes with EDTA, of which 28 were additionally submitted in tubes with lithium heparin. Based on clinical and laboratory parameters, the samples  were claThe sensor used has been developed as part of the ViSQCT project of the Bioinstrumentation and Nanomedicine Laboratory (LBN) of the UPM. A complete description can be found in . Its basEquation ) is obtaEquation ) 40]. T. Tfs) ofe sample .(1)Δf=fThis work was performed using QCR with The experimental setup is illustrated in p-values less than 0.05 were considered statistically significant. The predictive abilities regarding septic SF of Statistical analysis was performed with SPSS, statistical software. Means are expressed as mean ± standard deviation. Mann–Whitney U was used for analytic comparison; ANNs are a case of Artificial Intelligence (AI) that, based on examples, can induce concepts. They are data processing systems whose operation is based on the networks of neurons in the brain ,32. ThesThe ANN was applied using the algorithm illustrated in the diagram in The dataset size was 4972 data for samples in tubes with EDTA and 5248 for samples in tubes with lithium heparin. After loading the input data, the data were randomly segmented for the training, validation, and test phases as 70, 15, and 15%. Thus, 70% of the dataset was used for training, 15% was used for validation, and the remaining 15% was isolated for testing with the trained model. This way, we had three datasets: training, validation, and test. The training dataset contained the examples used during the learning process and was used to adjust the parameters. A validation dataset was a set of examples used to adjust the hyperparameters. The test dataset was a separate dataset from the training dataset used to test the model after training. After the data splitting, the data were optimized by scaling them to a range of values between 0 and 1. A robust scaler was employed, which scales the information according to the quantile range, making it robust against outliers. The accuracy value was obtained for each case to observe the algorithm’s performance. Accuracy is obtained from the fraction of the total number of correct predictions divided by the sum of all predictions (Equation ):(3)AccuFinally, to compare different ANN configurations, the HL of the networks were varied between 1 and 2 layers, and the number of training epochs among 100, 200, and 300. These configurations are shown in All algorithms were developed using the Keras and sci-kit learn libraries in Python. We used a linear kernel for SVM, c = 1, loss = “squared hinge.” For RF, we used 2171 trees, minimum sample split = 2, maximum depth = 200, and criterion = “gini.” The hyperparameters for the RF model were established by a previous exploration (tuning) with a grid search. For this, a range of values was defined, and a search algorithm performed a random search of those values and found the best one. The default setting was used for the SVM model while adding the “squared hinge loss,” which is common for binary classifications . For theConcerning the parameters measured with the sensor, there were no statistically significant differences between the mean values of ot for η . When loThe predictive ability of each parameter is shown in In The obtained parameters showed slightly better performance in samples stored in tubes with lithium heparin; nevertheless, they are far from being decisive for classification. One study  showed tWhen observing the results, it is noticeable that the SF samples contained in tubes with lithium heparin showed higher enomenon ,44.Based on the low performance of each parameter individually in differentiating SF precisely, there was interest in testing AI algorithms to see if they can help better classify the samples. When classifying by ANN, six scenarios were analyzed for each container case of the SF samples. When viewing the accuracy values obtained in This work showed that the technique used to characterize the viscous properties of SF using a QCR-based sensor could help classify and differentiate infectious SF from other nosological entities. The results are encouraging; however, a more extensive study is needed. We have shown an ANN that aids in the classification of inflammatory and infectious SF using data associated with the viscous properties of SF obtained using a QCR sensor. The extraordinary ability of AI technologies to classify data in a way that is superior to conventional techniques was demonstrated. In the comparison carried out in this work, the improvements through the use of classification models such as random forests and neural networks were noticeable. When comparing both classifications of SF using


The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired  An earlier cancer diagnosis reduces the probability of death in cancer patients, which can be achieved using comprehensive screening programs [In recent years despite the technological advances in imaging tools, the earlier detection of BC has remained a tenacious challenge. According to a recent statistical analysis, BC is the most predominant cancer in women and the second most common cause of mortality in undeveloped and developed countries  problem executed on a high-throughput multi-dimensional microarray gene expression dataset. The number of features (genes) is significantly greater than the number of instances in the high throughput gene expression microarray dataset. Thus, many features lead to overfitting problems  where the standard ML algorithms overfit the data, leading to a significant performance difference between the trained and tested supervised classifier-based predictive models ,19. TherTypically, FS approaches can be grouped into four groups, namely, (1) filter, (2) wrapper, (3) embedded, and (4) hybrid methods . Filter Feature selection in a high ratio of features to samples in microarray gene expression data is an example of an NP-hard problem. Therefore, heuristic-based global minima search algorithms are most appropriate for finding the most optimal solutions in these complicated NP-hard problems. Moreover, the hybrid FS methods are sometimes more applicable than filter-based methods in screening gene biomarkers from the cancer microarray gene expression dataset . The natMotivation: FS studies applying metaheuristics in the hybrid learning approach for gene biomarker selection are still limited in BC classification, thereby enabling an earlier diagnosis of BC. Thus, the impact studies involving the hybrid approaches using heuristics algorithms require further research. These facts motivated us to propose a new framework involving a systematic, comprehensive analysis of the other hybrid FS methods, followed by various classifier-based primary tumor prediction analyses to screen genes that have robust diagnostic efficiency in detecting the earlier stages of BC using the microarray gene expression dataset. Moreover, our study also provides biological insight into the selected subset of features. Thus, our method aims to determine genes with robust diagnostic capability while also providing biological insights that would help understand the early molecular mechanisms of BC from the topological and physical aspects.The first stage involves systematically applying the filter-based, statistical, and metaheuristics optimization approaches to select the best subset of the predictor.The second stage consists of the classification stage. In this stage, the screened subset of genes from the FS stage was used to identify BC features with the highest classification accuracy.In the present study, we propose a new framework of a hybrid feature selection approach to screen predictors from microarray gene expression data with better diagnostic efficacy in predicting the earlier stages of BC. In this context, our proposed hybrid-based FS approach consists of two steps.We compared our proposed hybrid FS method with the state-of-the-art techniques developed in recent years.The experimental results showed that the hybrid FS method presented in this study is robust in classifying primary BC tumors into binary categories , outperforming the state-of-the-art hybrid FS methods developed to screen gene biomarkers for an earlier diagnosis of BC.Contribution: The main contributions and the novel aspects of the present study are the following:In the present study, a machine learning-based hybrid FS framework was developed to screen the best optimal feature subset to build a classification model for earlier detection of primary BC, as represented in The hybrid FS method was designed to screen a stable and relevant subset of stable gene biomarkers (features) to classify primary breast cancer samples from normal breast samples. The hybrid FS approach yielded the five most optimal subsets of features. First, five subsets of the gene microarray data were generated using the five feature subsets obtained using the hybrid FS approach. Next, the seven state-of-the-art supervised classification algorithms, namely SVM, KNN, NN, NB, DT, XGBoost, and LR, were trained and tested on the five subsets of the microarray data using ten-fold cross-validation to test the predictive potential of the selected subsets of gene biomarkers and thereby determine the most efficient breast cancer diagnostic model with higher accuracy, F1-Score, and AUC value. Finally, the supervised classification model’s predictive ability was estimated using a set of statistical matrices whose mean and standard deviation values are documented in the result section of the present article. The remaining part of this research article is organized as follows: Many Hybrid FS methods involving heuristic algorithms have been developed in recent years to resolve the breast cancer classification problem. Here, we present and discuss the recently developed hybrid FS methods available in the literature.As shown in The method combines a Fisher Score-based filter and the Ant Colony Optimization (ACO) algorithm. The Hybrid FS method was named the Hybrid fisher-Ant Colony Optimization (HFACO) algorithm. The combination of HFACO with the C5.0 classifier showed high classification accuracy. Ghozy et al.  proposedThe experimental result showed that the authors could screen 49 gene biomarkers to classify breast cancer with an accuracy of 85.63%. AbdElNabi et al.  proposedLu et al.  proposedk-neighbors = 5, and n_jobs = None. In the transformed Breast_GSE22820 dataset, upon oversampling, the number of samples of each class (encoded as 0 and 1) is balanced . The balanced data is divided into 80 % training and 20 % testing data. The Breast_GSE22820 gene expression microarray data were used for hybrid FS, and supervised classification models were downloaded from the CuMiDa database . The Bret-test, and five state-of-the-art meta-heuristics algorithms to screen the most optimal and stable subset of features for the classification objective, depicted in The proposed hybrid FS method involves an mRMR, a two-tailed unpaired t-test was performed to compare the mean of the distribution among two classes  of the twenty most relevant and nonredundant features screened using mRMR at a 5% significance level. Next, a subset of data was generated, including the class variable and features with a significant mean difference between the two classes (groups) at a 5 % significance level. Thirdly, five state-of-the-art meta-heuristic algorithms were used to screen the most optimal feature subsets from a subset of features whose average distribution values are significantly different between the two classes (groups). Finally, the five most optimal feature subgroups obtained using the five meta-heuristic algorithms were trained and tested using stratified ten-fold cross-validation on various supervised classification algorithms to develop supervised ML models that can classify the primary breast cancer samples from the normal breast cancer sample with higher accuracy, F1-Score, and AUC value.In the proposed hybrid FS method, firstly, the multivariate filter-based mRMR technique using the F-statistic with a Correlation Quotient (FCQ) scoring scheme was used to screen the most relevant and nonredundant feature subset. Eventually, a subset of the twenty most relevant and nonredundant features was selected based on the FCQ score. A subset of the original data, including the twenty-one features (20 independent features + 1 dependent variable), was then generated and used for further analysis. Secondly, a two-tailed unpaired The mRMR  is a minh can be chosen as the maximum relevance score. The F-test value of gene (feature) ig in K classes represented by h is written as Equation (1):For continuous attributes , the F-statistic between the genes and the target variable kth class, K is the number of classes, and kth class). Using the relation F-test will reduce to the t-test for the two-class classification problem. Consequently, for the gene biomarker set S, the maximum relevance can be represented as:Here The present study specifies the minimum redundancy condition between the input variables using the Pearson correlation coefficient F-test with Correlation using Quotient, which is written as:Next, the following features are screened using the mRMR optimization criterion function involving FCQ. Finally, in Equation (4), we combine N is the number of genes in the whole gene set, The precise solution to the MRMR requirements needs exploration (S = 20) as the size of the minimal-optimal feature subset. Subsequently, a data subset was generated comprised of twenty genes (features) from the mRMR algorithm, which was further analyzed using a Student’s t-test to select features whose mean significantly differed between the target class sample populations .In the present study, we selected the top 20 features , Binary Bat Algorithm (BBA), Cuckoo Search Algorithm (CSA), Red Deer Algorithm (RDA), Genetic Algorithm (GA), were used to obtain the most optimal feature subset from the subset of a dataset comprising the significant features set attained using a two-tailed unpaired Binary Bat AlgorithmThe bat moves in a search space toward continuous-valued locations. However, in the selection, the bat travels across the corners of a hypercube since the search space is modeled as an 𝑛- dimensional Boolean framework . MeanwhiSuppose for each bat bi an initial position 𝑥𝑖, velocity 𝑣𝑖, and frequency 𝑓𝑖 are initialized. Then, for an individual iteration, say 𝑡, where 𝑇 is the maximum number of iterations, the movement of the virtual bats is represented by revising their position and velocity by employing Equations (6)–(8), as shown below:Here 𝛽 signifies an arbitrarily created number between 0 and 1. It signifies the value of the decision attribute denoted by 𝑗 for a given bat 𝑖 at an iteration 𝑡. The outcome of Here 2.Genetic AlgorithmThe GA is a heuristic search-based optimization algorithm inspired by Charles Darwin’s theory of evolution and natural selection. The GA is used to find the optimal or near-optimal solution to NP problems that are difficult to solve ,55,56. I3.Equilibrium Optimizer AlgorithmThe EO algorithm is a new meta-heuristic algorithm developed in 2019 for solving single-objective optimization problems . EO usesO = Big − O notation is used as common terminology, t = a number of iterations, n = a number of particles, d = number of dimensions, and c = cost of function evaluation.Here, 4.Cuckoo Search AlgorithmThe cuckoo search is a metaheuristic optimization algorithm developed for solving optimization problems ,59. It iIn a straightforward case, we assume that each nest holds one egg. An individual egg in a nest signifies a solution, and an additional cuckoo egg represents another solution. The CS algorithm aims to replace a not-so-good solution (host egg) in the host nest with a better solution (cuckoo egg). The algorithm can be further expanded to a more complex scenario where each nest consists of many eggs representing multiple solution sets.Additionally, the CS algorithm is built on three central rules:An individual cuckoo lays only one egg at a time and leaves its egg in an arbitrarily selected nest of a host species;The finest nests with superior-quality eggs will be passed down to the subsequent generation;The number of accessible hosts and their corresponding nests are set, and the probability of an egg laid by a cuckoo to be discovered by the host bird is given as follows:In the present scenario, the host bird can put the cuckoo egg away or desert the nest, or the host can construct a new nest.αp more in the CS algorithm along with the parameter, which is the population size n. Thus, the CS algorithm is significantly simpler in implementation.A significant benefit of the CS algorithm is its simplicity. Compared with other agents- or population-based meta-heuristic algorithms, namely harmony search and particle swarm optimization, there is an additional single parameter 5.Red deer Algorithmm). The remaining are classified as hinds (Nf) using Equation (12).The Red Deer algorithm (RDA) is a population-based meta-heuristic algorithm . The RD r is a continuous random variable between 0 and 1Where The stages are employed to exploit feature space, and the hinds are utilized to explore the feature space. Exploitation by stags is confirmed by assessing the nearby stags made using Equation (13) given below.r1, r2, and r3 signify continuous random values ranging from 0 to 1. The current fittest solution (The Upper Boundary (UB) and the Lower Boundary (LB) of the search space represent the highest number of features that can be selected and the lowest number of features that must be selected, respectively. The variables c” signifies a continuous random variable with its value ranging from 0 to 1. The breeding process results in an acceptable level of exploration or diversification of all possible solutions. Moreover, the present commander will fight with randomly chosen stags in the feature space (population). The stag who defeats the current commander will become the next commander . Next, the fitness value of all the existing stags is calculated again. The process endures until the commander deer obtains the maximum fitness value or the greatest number of deer is generated. The pseudocode of the feature selection process used in the RD algorithm is shown below in All FS experiments were performed on a laptop with an Intel Core i7 processor with 2.4 GHz and 8 GB of RAM. Each of the five subsets of the gene microarray dataset generated from the five optimal feature subsets obtained after the hybrid FS techniques was partitioned into 80% training and 20% independent testing datasets. Each of the 80% training datasets consists of 282 samples, of which 141 samples belong to primary breast cancer, and the other 141 samples belong to normal breast samples, as shown in Subsequently, stratified ten-fold cross-validation on the 20% testing datasets was performed to assess the classifying proficiency of each ML-based trained model. All five 20% testing data derived from the five heuristics FS method consist of 35 samples belonging to the primary breast cancer samples and the other 35 samples belonging to the normal breast samples, as shown in The seven typically used supervised classification algorithms used for the training of the seven different classification models are discussed below:Logistic regression is a supervised learning classification algorithm. The LR classifies discrete target variables based on a sigmoid function, where the independent variable’s values lie between +∞ to −∞, and the output is a probabilistic value of a target variable that ranges between 0 to 1 ,64. The Extreme Gradient Boosting, abbreviated as XGBoost, is an accurate implementation of distributed gradient-boosted decision tree (GBDT). The XGBoost algorithm implements a parallel boosting tree and is the most popular method for solving classification, ranking, and regression problems . XGBoostkth DT, and F represents the set of each K score for the whole classification DTs. The result of the model is improved using regularization and is shown below using the following equation:Here, Here, the first part of the equation measures the difference between the target and the predicted n T, the weight of each leaf is represented by w, and the constants regulating the degree of regularization are represented by γ, and λ, respectively.Here, the quantity of leaves in the tree is denoted by The GNB is a modified version of NB. Gaussian distributions ,67 are aThe Gaussian PDF takes the form of a bell and can be represented by the following equation:μ represents the mean, and σ2 is the variance. In GNB, we must define a normal or Gaussian distribution for each continuous variable. The parameters of such normal distributions can be obtained using the following formulae:Here, cN is the number of instances where C = c and N represents the total number of training instances. Calculating P (C = c) for the target variable is performed by applying relative frequencies by using the following equation shown below:Here, DT is a nonparametric supervised machine learning algorithm that generates a classification model that classifies the target variable by evaluating a tree of true/false or if-then-else independent variable questions. The DT algorithm estimates the least number of variable questions needed to reach the decision point (leaf node). The DT algorithm can be used as a regressor to predict a continuous numeric value of a target variable or as a classifier to classify a categorical target variable . Our stuip is the probability of an instance being classified by the DT algorithm to a particular class label.k”, which tells us about the number of “nearest neighbors” or data points from the training data. The nearest neighbors are found based on the nearest distances from the query data point. Euclidean Distance (ED) techniques calculate the distance between the two data points. After finding the k closest data points or neighbors, the algorithm executes a majority voting rule to find the target variable with maximum appearances (hits). The class label with maximum appearances is declared to be the final classification class label for the query. The equation for the calculation of the distance function (Euclidean distance) is shown below:The classic KNN algorithm is a nonparametric supervised classification technique applied to solve classification problems . The algx) and an existing data point (y).The ED is calculated as the square root of the sum of the squared differences between a new data point  class label. The training set of the original data is divided into smaller groups, and each subgroup builds an individual decision tree in an RF. Each DT generates an outcome . The claSuppose, for a nominal splitting feature, The final parameter settings used to construct each ML model are shown in The classifying capability of the supervised classification models was evaluated using the following classification model performance metrics:Accuracy plays a vital role in assessing classification models in a scenario where the samples of each class are equal in number. In our study, the test dataset is balanced. Therefore, accuracy offers a better insight into the model’s capability to classify the target class. The accuracy value ranges from 0 to 1, where the model with an accuracy value of 1.0 is considered the best-performing model. On the contrary, the model with a value ‘of 0.0’ is considered the worst-performing model. Mathematically, the accuracy is determined using the following formula:TP, TN, FN, and FP, respectively. Moreover, a confusion matrix defines the classification model’s performance on testing data for which the actual values are well-known. The related terminologies from the confusion matrix are defined as follows:In Equation (23), true positive, true negative, false negative, and false positive are abbreviated as Positive class .Negative class .True positive is a classification outcome where the classification model correctly classifies an actual positive instance (primary breast cancer) as a positive class sample.A true negative is a classification outcome where the classification model correctly classifies an actual negative instance  as a negative class sample.A false positive is a classification outcome where the classification model incorrectly predicts a positive class sample as a negative class.A false negative is a classification outcome in which the model incorrectly predicts an actual positive class instance as a negative one.Furthermore, a confusion matrix explains the classification models’ error types (type I and II) . The preThe F1-Score is defined as the harmonic mean of precision and recall and is calculated using the following mathematical formula:TPR) and the False Positive Rate (FPR) at decision threshold values (ranging from 0 to 1) and essentially separates the ‘noise’ from the ‘signal’. The TPR and FPR are calculated using Equations (25) and (26), respectively.The Receiver Operator Characteristic (ROC) curve is a metric typically used to evaluate a binary classification problem. The ROC is a probability curve that plots a two-dimensional graph between the True Positive Rate  and finishing at , thereby allowing an accurate measurement of the model’s ability to differentiate the two class labels in the gene microarray dataset. The higher the AUC value, the better the implementation of the classification model to distinguish the positive and negative classes in a two-class classification problem. For example, a classification model with an AUC value of 1.0 can accurately differentiate between the two-class labels in a dataset. If, however, the AUC value is zero, then the classifier predicts all actual positive samples as negative and all true negative samples as positive.The histogram frequency curve plot describes the frequency distribution of continuous attributes belonging to the corresponding two class labels. In our study, the plot represents the frequency distribution of genes (continuous variables) between the two class labels population in the Breast_GSE22820 dataset. Additionally, the histogram frequency curve represents the difference in the population mean of the continuous attributes (genes) between the two class labels .t-test [We performed a one-tailed unpaired t-test  to evaluRender is a cloud-based platform used to host our XGBoost-based classification application. The output of our application is a probabilistic score.t-test, and five state-of-the-art nature-inspired meta-heuristic methods, namely EO, BBA, CSA, RDA, and GA, was employed to screen a potential set of gene biomarkers for the earlier detection of primary breast cancer.Despite the recent development in imaging technologies, the earlier detection of BC remains a challenge for 21st-century researchers. In the current setting, screening potential genes involved in the pathophysiology of BC will provide researchers and clinicians with a precise method to detect BC in the earlier stages of its existence in the human system. In this context, screening potential gene biomarkers using various FS techniques from the gene expression microarray data of BC patients has been proven helpful in recent times in understanding the molecular aspects of BC. Therefore, in this regard, screening potential gene biomarkers for accurately classifying primary breast cancer patients will enable less but more reliable testing, resulting in faster and more effective diagnostics and prognostic procedures in managing BC patients. In the present context, in our study, a hybrid FS technique was executed to screen potential gene biomarkers to classify primary breast cancer samples from the gene microarray data. A novel hybrid FS framework of a multivariate filter-based method, a Student’s The mRMR method selects a subset of features with minimum redundancy and maximum relevance with the target variables. The FCQ scoring variant of mRMR was used to calculate the correlation between continuous variables (genes) and the relevance of the variables to the target variable of the Breast_GSE22820 gene expression dataset. The top twenty features obtained using the FCQ scoring variant of the mRMR have been tabulated in t-test at a 5% significance level was executed to screen features obtained from the mRMR algorithm, demonstrating a significant difference between the mean value of each twenty features between the two classes’ sample populations. The results of the two-tailed unpaired t-test of the mRMR selected the top twenty gene biomarkers evaluated at a p-value < 0.05 and is shown in The two-tailed unpaired t-tests, were selected and further screened using five state-of-the-art metaheuristic algorithms to obtain an optimal subset of features capable of discriminating the target variables with higher performance.The Gene Biomarkers (features) whose frequency distribution mean was significantly different (at a 5% significance level) between the two classes of labels that were selected are shown in The present study comprehensively analyzes the five nature-inspired global optimization algorithms for optimal FS. The present study implements five state-of-the-art optimization algorithms to find the five optimal feature subsets out of the entire search space . The EO metaheuristic method yielded an optimal subset with three features: ‘NM_138957’, ‘NM_152426’, and ‘NM_001008493’. The BBA-based metaheuristic identified an optimal subset of two features, namely ‘NM_152426’ and ‘NM_138957’. Similarly, using the CSA algorithm, we obtained an optimal subset of two features, such as BC016934 and ‘NM_006579’.Using the RDA metaheuristic method, we obtained an optimal feature subset with one feature, namely BC016934. Lastly, an optimal subset with one feature, such as NM_152426, was obtained using the GA algorithm. The five optimal subsets screened using the five global optimization algorithms are listed in Each of the five optimal subsets of feature(s) obtained using five metaheuristic algorithms were trained and tested on seven supervised classification algorithms, namely DT, NB, SVM, XGBoost, LR, KNN, and RF, using the five data subsets prepared using the five optimal subsets of features obtained from the post-metaheuristic-based FS. In addition, stratified five-fold cross-validation was applied to train and test classification models in each training and independent test dataset. The present study aims to screen the best-performing classification model in distinguishing the target variable labels built using the most optimal subset of the gene biomarker to facilitate the earlier detection of BC patients. We can observe from the comparative performance evaluation of the seven classification models that the XGBoost-based model developed using the optimal feature subset obtained from the EO algorithm is the best classification model with accuracy (0.976 ± 0.027), F1-Score (0.974 ± 0.030), and AUC (0.987 ± 0.025) shown in t-test and base classifiers, namely SVM, NB, DT, LR, KNN, RF, and XGBoost, as represented in A summary of the baseline model performance of the eight models built using all of the features in the COVID-19 dataset is listed in p < 0.001, as shown in Moreover, using a histogram plot, the mean difference in the frequency distribution of the best-performing gene biomarkers  between the two classes  is represented in t-test, a statistical method, and metaheuristics algorithms achieved the highest accuracy (0.976 ± 0.027) and AUC value (0.987 ± 0.025) as compared to classification models built using other gene biomarker-based classification models, as shown in Our proposed XGBoost-based classification model performance was compared with the performances of the classification models built using different hybrid feature selection approaches involving filter, wrapper, and other FS methods, as tabulated in https://appbcgene.onrender.com/, accessed on 1 December 2022. In the real-time online primary breast tumor detector, the users can classify primary breast tumor samples from normal breast samples using the gene expression values of the genes, namely MAPK-1, APOBEC3B, and ENAH actin regulator (ENAH). Therefore, clinicians can use our online breast tumor predictor to detect BC in its earlier stages.The XGBoost-based classification model was implemented as a web application and hosted at t-test). Finally, the subset of statistically significant features (gene) obtained using the two-tailed unpaired t-test was processed) using the five state-of-the-art metaheuristic algorithms . Various supervised classification algorithms, namely SVM, NB, DT, LR, KNN, RF, and XGBoost, were used to screen the final most optimal feature subset of predictors for classifying primary breast tumors from normal breast gene expression microarray data. The current approach is a comprehensive analysis of a hybrid approach involving a sequential application of a filter-based method, statistical, and metaheuristics for gene FS for earlier detection of primary breast tumors. Compared to other hybrid FS methods and ML models, as tabulated in Screening robust gene biomarkers from gene microarray data for earlier detection of BC has long been a challenging problem due to the low sample size and multi-dimensionality properties of gene microarray data. We used hybrid learning in our FS framework to screen robust gene biomarkers. We systematically evaluated five hybrid FS approaches by introducing a sequential FS pipeline comprising a filter-based method (mRMR) followed by a statistical method /vasodilator-stimulated phosphoprotein (VASP) family of proteins. It assembles actin filaments for cell motility and adhesion . Recent These concepts elucidate the role of three gene biomarkers, namely MAPK 1, APOBEC3B, and ENAH, in tumorigenesis and potential use as diagnostic and prognostic gene biomarkers of BC. Moreover, these elucidations will significantly influence the therapeutic approaches to managing BC. Thus, our hybrid FS method successfully selected genes that have biological insight and that are potentially druggable targets ,95,96The three best gene biomarker-based ML models were built and tested, and their classification abilities were measured using various statistical metrics. The trained classification models were tested on the independent test data with a balanced distribution of instances in the two classes . The XGBoost-based classification model accuracy, F1-score, and AUC value were better than the other classification models, as shown in https://appbcgene.onrender.com/, accessed on 1 December 2022.The comparative study signifies that the three gene biomarkers obtained using our novel hybrid FS pipeline are highly relevant in classifying primary breast tumors with greater accuracy, thereby enabling the use of the current biomarker-based XGBoost model in the earlier detection of breast cancer. Furthermore, our proposed XGBoost-based web application for classifying primary breast tumors from normal breast gene expression data has been successfully implemented. The web application is available on Render at Using novel FS and ML approaches to identify potential gene biomarkers could help classify primary breast tumors. Furthermore, the early detection of BC will help slow down BC’s progression and potentially improve the survival rate through therapeutic interventions at a precise time. To conclude, we propose that, using a robust hybrid FS method, the present study identified a robust and optimal set of three gene biomarkers  for detecting the primary breast tumors of BC patients. Furthermore, the ontology studies show that the gene biomarkers screened using the hybrid approach have biological insight and play an essential role in breast tumorigenesis. Thus, it could be used as BC’s diagnostic and prognostic marker.Moreover, in the future, applying a different pipeline of hybrid FS approaches and having access to a large sample size gene expression dataset from different institutions (multi-center study) and screening key features from various relevant data sources could be used to develop an enhanced model to classify primary breast tumors from normal breast samples. However, the proposed hybrid feature selection framework achieved state-of-the-art accuracy in classifying primary breast tumors from the gene expression data. However, there are still some limitations in the present study. First, the dataset used in the present study is a binary class data, and the current approach may not give the same response as a multiclass dataset. In addition, the proposed framework was applied to a smaller size of training data. Therefore, we need to apply the novel hybrid FS framework to a larger training sample size to evaluate the robustness of the hybrid FS framework in screening the best optimal solution from a given feature space.


Cancer prognosis is an essential goal for early diagnosis, biomarker selection, and medical therapy. In the past decade, deep learning has successfully solved a variety of biomedical problems. However, due to the high dimensional limitation of human cancer transcriptome data and the small number of training samples, there is still no mature deep learning-based survival analysis model that can completely solve problems in the training process like overfitting and accurate prognosis. Given these problems, we introduced a novel framework called SAVAE-Cox for survival analysis of high-dimensional transcriptome data. This model adopts a novel attention mechanism and takes full advantage of the adversarial transfer learning strategy. We trained the model on 16 types of TCGA cancer RNA-seq data sets. Experiments show that our module outperformed state-of-the-art survival analysis models such as the Cox proportional hazard model (Cox-ph), Cox-lasso, Cox-ridge, Cox-nnet, and VAECox on the concordance index. In addition, we carry out some feature analysis experiments. Based on the experimental results, we concluded that our model is helpful for revealing cancer-related genes and biological functions. Since the 20th century, cancer has become a serious threat to human life and health. Multi-angle and multi-level prognostic studies on cancer emerge one after another. However, prognostic research for cancer is still a challenging task. One of the most important factors is that due to the existence of censored patient samples, the traditional analysis models cannot effectively determine the actual time of death . CompareThe arrival of the Human Genome Project has raised the survival sub-model to the research category of high-throughput multi-omics . There aWith the maturity of deep learning methods in different fields ,12, the The high dimensionality and complex semantics of genetic data bring many challenges to feature extraction. So far, there is a lot of work that is trying to use new ideas to ensure rich feature extraction. The most classic method in deep learning was multilayer perceptron (MLP), which learns linear correlations between different data. It was considered to be the optimal choice for processing sequential data. There are a lot of works to extract high-dimensional genetic data based on MLP ,16,19. ISince the Generative Adversarial Network (GAN) proposed by Ian et al. , a lot ohttps://github.com/menggerSherry/SAVAE-Cox (Last visited on 21 March 2022).In this paper, we propose a novel deep Self Attention Variational Autoencoder Cox Survival Analysis Model (SAVAE-Cox). This model takes advantages of adversarial transfer learning strategy. In the adversarial pretraining stage, the generator was a variational autoencoder (VAE), which is jointly trained with the discriminator. Meanwhile, we introduce a novel self-attention mechanism  to enhanIn this work we used 17 datasets from the TCGA database. These 17 datasets are bladder carcinoma (BLCA), breast carcinoma (BRCA), head and neck squamous cell carcinoma (HNSC), kidney renal cell carcinoma (KIRC), brain lower-grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian carcinoma (OV), stomach adenocarcinoma (STAD), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), sarcoma (SARC), uterine corpus endometrial carcinoma (UCEC), prostate adenocarcinoma (PRAD), skin cutaneous melanoma (SKCM) and pan-cancer (PANCAN). The detailed description of the dataset and download way were in We use the PANCAN dataset to draw scatter plots of 56,716 genes and observe the statistical distribution. We adopted the strategy of a generative adversarial network (GAN)  to desigThe network structure of the We introduce a residual self-attention module  in the hIn Equation (5), n Module . It is dL1 loss to measure the similarity of each sample. The L1 loss is computed as:We introduce the Wasserstein loss  to trainvergence  used in L1 loss focuses on making Unlike the Wasserstein loss and KL divergence, the After the pre-training stage, we transfer the weights learned by the encoder in the pre-training stage to the survival analysis stage as shown in We adopt the training strategy of Cox-ph  to trainWe implemented our model using the PyTorch framework. We train and validate our model using an Nvidia Tesla V100 (32GB) GPU. We first pre-train SAVAE using the pan-cancer database. To validate the pre-trained reconstruction results, we randomly divide the training dataset and the test dataset with a ratio of 9:1. For survival analysis on 16 cancer datasets, we trained our model by dividing the dataset into five-fold cross-validation. Both stages are trained using the Adam optimizer. In the pre-training stage, the learning rate of the optimizer is 0.0001, the total epochs are 300, and the batch size is 256. At the same time, since the loss fluctuation problem often occurs in the training process of GAN, it is necessary to set the learning rate decay strategy. Therefore, we stipulate that the learning rate remains constant for the first 150 epochs, and decays linearly to 0 for the last 150 epochs. In the SAVAE-Cox training phase, the learning rate of the optimizer is 0.001, the total training epochs are 20, and the batch size is 512. Note that there are some hyperparameters such as learning rate,In this work, the evaluation method we mainly use is the concordance index , which iGenerator can reconstruct The reconstruction of the generator is based on the latent encoding In To further verify the superiority of our proposed dimensionality reduction method, we compare the performance with other dimensionality reduction methods. The comparison results were shown in By comparing these six methods on 16 cancer types, our dimensionality reduction method performed best in nine of them. Interestingly, using the Chi2 feature selection-based dimensionality reduction method outperforms data-driven methods in LUSC, CESC, and SKCM datasets. As shown in We use the concordance index to evaluate the performance of SAVAE-cox models. p-value, we can find that the hazard ratios predicted by SAVAE-Cox have a more significant impact on patient survival than that predicted by Cox-nnet.We performed further survival analysis on 12 cancer types. Based on the hazard ratios of patient samples predicted by SAVAE-Cox, we can calculate the mean reference hazard ratios for 12 cancer types. For each cancer type, we defined patients with predicted hazard ratios above the average to be in the high-risk group, and patients with predicted hazard ratios below the average to be in the low-risk group. In this way, we divided patient samples into high-risk and low-risk groups for each cancer type. Therefore, we draw Kaplan–Meier (KM) survival curves for different cancer types according to these two groups. At the same time, we adopted the same strategy to plot the KM survival curve of the Cox-nnet prediction results. The incidence of BRCA is very high, so there were a lot of related studies on this malignancy. At the same time, benefiting from the professionalism of the TCGA project, the researchers collected abundant samples. Choosing BRCA for survival analysis can obtain the most stable survival analysis results and conclusions. Therefore, we take BRCA as an example to conduct experiments for further correlation analysis between model features and genes. All of the 1031 patient samples in the BRCA dataset were tested in this study. First, we analyzed the hidden layer nodes that contribute the most to the prognosis according to the mean and variance. After analysis, we selected the top 20 key prognostic hidden layer nodes. Finally, we calculated a Pearson correlations matrix for each node and gene expression across the patient samples. According to the calculated correlation matrix, we can analyze the correlation between hidden layer nodes and different genes.https://diseases.jensenlab.org/, Last visited on 21 March 2022) and plotted them in We selected 34 cancer-related genes from DISEASE  using the KEGG pathway. We ranked genes according to the Pearson correlation and selected the leader gene for each key node. Based on the leader genes, we created the pathway association network . In FiguTo verify the contribution of our proposed model to the effect of survival prognosis, we performed ablation experiments on SAVAE-Cox. Briefly, we divided the model into four groups: Cox-nnet, SAVAE-Cox without pretrain, SAVAE-Cox without attention, and SAVAE-Cox. We trained four models on 16 cancer types and divided ranks 1, 2, 3, and 4 in descending order according to the performance of the four models in each cancer and plotted By comparing SAVAE-Cox and SAVAE-Cox without attention, we find that using the self-attention module can significantly improve the model prognosis accuracy. This result shows that there is a potential feature semantic correlation in high-dimensional gene expression, and this correlation cannot be effectively learned using traditional fully connected layers. This latent relationship can be found to a large extent using attention-based methods. By comparing SAVAE-Cox and SAVAE-Cox without pretrain, we find it interesting that the module that used transfer learning on the five datasets HNSC, KIRC, LUAD, LUSC, and OV fails to improve the model’s prognostic results. In general, using the transfer learning strategy improves the C-index of the model.In this work, we introduced a novel survival analysis model for different cancer types, which is the first attempt to improve the overall survival analysis accuracy with the help of the self-attention mechanism. At the same time, we designed a data-driven dimensionality reduction method regarding the idea of transfer learning and GAN  to furthWe proposed an adversarial VAE-based  pre-traiThrough the analysis of BRCA, our model can discover cancer-related genes and reveal biological functions. From However, we found that the overfitting of the SAVAE-Cox is still a very serious problem. At the same time, the performance of the model is not good enough on datasets with an imbalanced number of positive and negative samples. In future work, we will study some data augmentation methods to solve these problems and explore some novel multi-head attention-based survival analysis frameworks.We introduced a brand new survival analysis model and performed survival prognosis on 16 cancer types. The prognosis was significantly improved when self-attention and transfer learning was integrated to the SAVAE-Cox. With the further analysis of the hidden layer features of SAVAE-Cox, we confirmed that the hidden layer features of this model play a significant role in cancer prognosis and the revealing of biological function. In conclusion, the SAVAE-Cox combines a self-attention mechanism with transfer learning, and feature selection provides a new prospect for future deep cancer prognosis.