Gene Selection Method Research Articles

Transcriptome-based prediction for polygenic traits in rice using different gene subsets

BackgroundTranscriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation. The selection of large-effect genes based on a priori biological knowledge is beneficial for predicting oligogenic traits; however, such a simple gene selection method is not applicable to polygenic traits because causal genes or large-effect loci are often unknown. Here, we used several gene-level features and tested whether it was possible to select a gene subset that resulted in better predictive ability than using all genes for predicting a polygenic trait.ResultsUsing the phenotypic values of shoot and root traits and transcript abundances in leaves and roots of 57 rice accessions, we evaluated the predictive abilities of the transcriptome-based prediction models. Leaf transcripts predicted shoot phenotypes, such as plant height, more accurately than root transcripts, whereas root transcripts predicted root phenotypes, such as crown root length, more accurately than leaf transcripts. Furthermore, we used the following three features to train the prediction model: (1) tissue specificity of the transcripts, (2) ontology annotations, and (3) co-expression modules for selecting gene subsets. Although models trained by a gene subset often resulted in lower predictive abilities than the model trained by all genes, some gene subsets showed improved predictive ability. For example, using genes expressed in roots but not in leaves, the predictive ability for crown root diameter was improved by more than 10% (R2 = 0.59 when using all genes; R2 = 0.66, using 1,554 root-specifically expressed genes). Similarly, genes annotated as “gibberellic acid sensitivity” showed higher predictive ability than using all genes for root dry weight.ConclusionsOur results highlight both the possibility and difficulty of selecting an appropriate gene subset to predict polygenic traits from transcript abundance, given the current biological knowledge and information. Further integration of multiple sources of information, as well as improvements in gene characterization, may enable the selection of an optimal gene set for the prediction of polygenic phenotypes.

ScPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

Nature-inspired computing based Non-Hodgkin lymphoma prediction from microarray expression with GA-RFE gene selection method; an experimental histopathological study

Nature-inspired computing based Non-Hodgkin lymphoma prediction from microarray expression with GA-RFE gene selection method; an experimental histopathological study

Robust and Effective: A Deep Matrix Factorization Framework for Classification.

For complex data, high dimension and high noise are challenging problems, and deep matrix factorization shows great potential in data dimensionality reduction. In this article, a novel robust and effective deep matrix factorization framework is proposed. This method constructs a dual-angle feature for single-modal gene data to improve the effectiveness and robustness, which can solve the problem of high-dimensional tumor classification. The proposed framework consists of three parts, deep matrix factorization, double-angle decomposition, and feature purification. First, a robust deep matrix factorization (RDMF) model is proposed in the feature learning, to enhance the classification stability and obtain better feature when faced with noisy data. Second, a double-angle feature (RDMF-DA) is designed by cascading the RDMF features with sparse features, which contains the more comprehensive information in gene data. Third, to avoid the influence of redundant genes on the representation ability, a gene selection method is proposed to purify the features by RDMF-DA, based on the principle of sparse representation (SR) and gene coexpression. Finally, the proposed algorithm is applied to the gene expression profiling datasets, and the performance of the algorithm is fully verified.

Comparative genomics analysis to explore the biodiversity and mining novel target genes of Listeria monocytogenes strains from different regions.

As a common foodborne pathogen, infection with L. monocytogenes poses a significant threat to human life and health. The objective of this study was to employ comparative genomics to unveil the biodiversity and evolutionary characteristics of L. monocytogenes strains from different regions, screening for potential target genes and mining novel target genes, thus providing significant reference value for the specific molecular detection and therapeutic targets of L. monocytogenes strains. Pan-genomic analysis revealed that L. monocytogenes from different regions have open genomes, providing a solid genetic basis for adaptation to different environments. These strains contain numerous virulence genes that contribute to their high pathogenicity. They also exhibit relatively high resistance to phosphonic acid, glycopeptide, lincosamide, and peptide antibiotics. The results of mobile genetic elements indicate that, despite being located in different geographical locations, there is a certain degree of similarity in bacterial genome evolution and adaptation to specific environmental pressures. The potential target genes identified through pan-genomics are primarily associated with the fundamental life activities and infection invasion of L. monocytogenes, including known targets such as inlB, which can be utilized for molecular detection and therapeutic purposes. After screening a large number of potential target genes, we further screened them using hub gene selection methods to mining novel target genes. The present study employed eight different hub gene screening methods, ultimately identifying ten highly connected hub genes (bglF_1, davD, menE_1, tilS, dapX, iolC, gshAB, cysG, trpA, and hisC), which play crucial roles in the pathogenesis of L. monocytogenes. The results of pan-genomic analysis showed that L. monocytogenes from different regions exhibit high similarity in bacterial genome evolution. The PCR results demonstrated the excellent specificity of the bglF_1 and davD genes for L. monocytogenes. Therefore, the bglF_1 and davD genes hold promise as specific molecular detection and therapeutic targets for L. monocytogenes strains from different regions.

Gene selection based on recursive spider wasp optimizer guided by marine predators algorithm

Detecting tumors using gene analysis in microarray data is a critical area of research in artificial intelligence and bioinformatics. However, due to the large number of genes compared to observations, feature selection is a central process in microarray analysis. While various gene selection methods have been developed to select the most relevant genes, these methods’ efficiency and reliability can be improved. This paper proposes a new two-phase gene selection method that combines the ReliefF filter method with a novel version of the spider wasp optimizer (SWO) called RSWO-MPA. In the first phase, the ReliefF filter method is utilized to reduce the number of genes to a reasonable number. In the second phase, RSWO-MPA applies a recursive spider wasp optimizer guided by the marine predators algorithm (MPA) to select the most informative genes from the previously selected ones. The MPA is used in the initialization step of recursive SWO to narrow down the search space to the most relevant and accurate genes. The proposed RSWO-MPA has been implemented and validated through extensive experimentation using eight microarray gene expression datasets. The enhanced RSWO-MPA is compared with seven widely used and recently developed meta-heuristic algorithms, including Kepler optimization algorithm (KOA), marine predators algorithm (MPA), social ski-driver optimization (SSD), whale optimization algorithm (WOA), Harris hawks optimization (HHO), artificial bee colony (ABC) algorithm, and original SWO. The experimental results demonstrate that the developed method yields the highest accuracy, selects fewer features, and exhibits more stability than other compared algorithms and cutting-edge methods for all the datasets used. Specifically, it achieved an accuracy of 100.00%, 94.51%, 98.13%, 95.63%, 100.00%, 100.00%, 92.97%, and 100.00% for Yeoh, West, Chiaretti, Burcyznski, leukemia, ovarian cancer, central nervous system, and SRBCT datasets, respectively.

Identification of oxidative stress-related biomarkers in chronic rhinosinusitis with nasal polyps using WGCNA combined with machine learning algorithms

Objective: To identify diagnostic markers related to oxidative stress in chronic rhinosinusitis with nasal polyps (CRSwNP) by analyzing transcriptome sequencing data, and to investigate their roles in CRSwNP. Methods: Utilizing four CRSwNP sequencing datasets, differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning methods for Hub gene selection were performed in this study. Subsequent validation was carried out using external datasets, as well as real-time quantitative polymerase chain reaction (Real-time qPCR), and immunofluorescence staining of clinical samples. Moreover, the diagnostic efficacy of the genes was assessed by receiver operating characteristic (ROC) curve, followed by functional and pathway enrichment analysis, immune-related analysis, and cell population localization. Additionally, a competing endogenous RNA (CeRNA) network was constructed to predict potential drug targets. Statistical analysis and plotting were conducted using SPSS 26.0 and Graphpad Prism9 software. Results: Through data analysis and clinical validation, CP, SERPINF1 and GSTO2 were identified among 4 138 DEGs as oxidative stress markers related to CRSwNP. Specifically, the expression of CP and SERPINF1 increased in CRSwNP, whereas that of GSTO2 decreased, with statistically significant differences (P<0.05). Additionally, an area under the curve (AUC)>0.7 indicated their effectiveness as diagnostic indicators. Importantly, functional analysis indicated that these genes were mainly related to lipid metabolism, cell adhesion migration, and immunity. Single-cell data analysis revealed that SERPINF1 was mainly distributed in epithelial cells, stromal cells, and fibroblasts, while CP was primarily located in epithelial cells, and GSTO2 was minimally present in the epithelial cells and fibroblasts of nasal polyps. Consequently, a CeRNA regulatory network was constructed for the genes CP and GSTO2. This construction allowed for the prediction of potential drugs that could target CP. Conclusion: This study successfully identifies CP, SERPINF1 and GSTO2 as diagnostic and therapeutic markers related to oxidative stress in CRSwNP.

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitating efficient dimension reduction and gene selection methods to accurately identify cancerous tumors. In response to this challenge, this study introduces an innovative strategy for microarray data dimension reduction and crucial gene set selection, aiming to enhance the accuracy of cancerous tumor identification. Leveraging DNA microarray technology, our method focuses on pinpointing significant genes implicated in tumor development, aiding the development of sophisticated computerized diagnostic tools. Our technique synergizes gene selection with classifier training within a logistic regression framework, utilizing a generalized Fused LASSO (GFLASSO-LR) regularizer. This regularization incorporates two penalties: one for selecting pertinent genes and another for emphasizing adjacent genes of importance to the target class, thus achieving an optimal trade-off between gene relevance and redundancy. The optimization challenge posed by our approach is tackled using a sub-gradient algorithm, designed to meet specific convergence prerequisites. We establish that our algorithm’s objective function is convex, Lipschitz continuous, and possesses a global minimum, ensuring reliability in the gene selection process. A numerical evaluation of the method’s parameters further substantiates its effectiveness. Experimental outcomes affirm the GFLASSO-LR methodology’s high efficiency in processing high-dimensional microarray data for cancer classification. It effectively identifies compact gene subsets, significantly enhancing classification performance and demonstrating its potential as a powerful tool in cancer research and diagnostics.

In Silico Identification of Effective Genes for Acute Leukemia Classification Using a Spline Regression-based Framework

Background: Microarray technology enables the examination of gene expression in thousands of genes and can be highly effective in identifying various types of cancers, including leukemia. However, many genes in microarray data are redundant and lack useful information for cancer diagnosis. The main objective of this study is to identify relevant and effective genes in classification of leukemia microarray data using a spline regression-based method, taking into account the correlation between genes.&#x0D; Materials and Methods: In this analytical study, leukemia microarray data are used to identify relevant genes in classification of leukemia into Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) using a spline regression-based gene selection method, called SRS3FS based on ℓ2,p-norm (0 &lt; p ≤ 1). Subsequently, the support vector machine (SVM) algorithm is employed to classify leukemia data into AML and ALL.&#x0D; Results: In this study, the classification results of SVM algorithm for 5, 10, 15, and 20 genes reveal that the SRS3FS method, employing ℓ2,1/4-norm, ℓ2,1/2-norm and ℓ2,3/4-norm, exhibited the highest accuracy of 97.06% when identifying 10 genes for distinguishing between AML and ALL. Moreover, the leukemia data was classified into AML and ALL with an accuracy of 100%, using a gene identified by the SRS3FS method based on ℓ2,3/4-norm and ℓ2,1-norm. The gene labeled as number 3252, annotated as GLUTATHIONE S-TRANSFERASE, MICROSOMAL, is recognized as the most important gene.&#x0D; Conclusion: The experimental results on leukemia microarray data demonstrate that the spline regression-based gene selection method can effectively identify relevant genes in classification and prediction of leukemia.

Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms

PurposeThe aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci. MethodsTo do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs in various cell subgroups was then evaluated by analysis of single cell samples from AAA. Additionally, we looked at the expression levels of the MFGs as well as the levels of inflammatory immune-related markers in cellular and animal models of AAA. Finally, we predicted potential drugs that could be targeted for the treatment of AAA. ResultsOur research identified 1249 up-regulated differential genes and 3653 down-regulated differential genes. Through WGCNA, we also discovered 44 genes in the blue module. By taking the point where several strategies for gene selection overlap, the MFG (ITGAL and SELL) was produced. We discovered through single cell research that the MFG were specifically expressed in T regulatory cells, NK cells, B lineage, and lymphocytes. In both animal and cellular models of AAA, the MFGs' mRNA levels rose. ConclusionWe searched for the AAA novel targeted gene (ITGAL and SELL), which most likely function through lymphocytes of the B lineage, NK cells, T regulatory cells, and B lineage. This analysis gave AAA a brand-new goal to treat or prevent the disease.

Abstract 7352: ScanCT: A tree-based machine learning model to detect single-cell genomic features associated with clinical outcomes

Abstract Single-cell technologies represent a revolutionary approach to resolving cell-type heterogeneity, identifying cells in specialized states, and detecting rare disease-associated cells. With the cost of single-cell technology decreasing substantially, its integration into clinical studies is gaining momentum. A new computational tool is needed to accommodate different single-cell genomics and clinical data formats while accounting for unwanted confounders. The study aims to develop a tree-based machine learning model to leverage the unprecedented resolution of single-cell multi-omics data for delineating the genomic and phenotypic drivers behind diverse immunotherapy responses. The proposed model is called single-cell analysis of Clinical Tree (scanCT), inspired by the Generalized Unbiased Interaction Detection and Estimation method for unbiased gene and protein feature selection and easy interpretation. The scanCT model learns from the data to select the genomic feature that best splits the cells from distinct clinical responses for each tree node. The confounding factors will be regressors in the nodes but not be used for branch splitting, while gene and protein features of interest will split the tree but not enter the regression model in each node. scanCT is built to be free from the biased selection towards variables of a larger number of categories or values. With tree-pruning and cross-validation, scanCT overcomes the over-fitting issue and enhances model generalization, especially for clinical studies with limited patients. Particularly, scanCT naturally fits the hierarchical cell type relationship and handles marker gene and protein interaction effects efficiently. Our approach was tested on single-cell datasets from B-cell malignancy patients undergoing Chimeric Antigen Receptor (CAR)-T cell therapy. The results from the scanCT are highly interpretable. For instance, each branch is a gene-protein combination profile, and cells are naturally partitioned by clinical association. The linear regressions at each leaf node are the clinical predictions for cells following the splitting criteria. The regression intercept is an average estimation of toxicity (e.g., neurotoxicity) or efficacy after controlling for confounder (e.g., tumor burden). scanCT accommodates categorical or continuous clinical response and survival data and is robust to missing values, a frequent challenge in oncological studies. scanCT represents a significant step forward in single-cell data analysis, which merges complex genotypic and phenotypic information with clinical outcomes. The efficacy and toxicity-associated genomic signatures will inform new manufacturing strategies to optimize CAR-T cell therapy products. The model and clinical association detections are expected to go beyond the B-cell malignancy field to benefit the broader cancer research community. Citation Format: Ye Zheng, Long Nguyen, Peigen Zhou, Alexandre V. Hirayama. ScanCT: A tree-based machine learning model to detect single-cell genomic features associated with clinical outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7352.

Bi-level gene selection of cancer by combining clustering and sparse learning

The diagnosis of cancer based on gene expression profile data has attracted extensive attention in the field of biomedical science. This type of data usually has the characteristics of high dimensionality and noise. In this paper, a hybrid gene selection method based on clustering and sparse learning is proposed to choose the key genes with high precision. We first propose a filter method, which combines the k-means clustering algorithm and signal-to-noise ratio ranking method, and then, a weighted gene co-expression network has been applied to the reduced data set to identify modules corresponding to biological pathways. Moreover, we choose the key genes by using group bridge and sparse group lasso as wrapper methods. Finally, we conduct some numerical experiments on six cancer datasets. The numerical results show that our proposed method has achieved good performance in gene selection and cancer classification.

Enhancing Cancer Classification Through Ensemble Machine Learning and Gene Selection Approaches

The vast dimensionality of gene expression data and the limited number of relevant genes necessitate the adoption of gene selection techniques. Furthermore, the choice of an efficient classifier plays a pivotal role in achieving accurate results. In this study, we employ the Minimum Redundancy Maximum Relevance (mRMR) method for gene selection, coupled with ensemble classifiers and individual classifiers like K-Nearest Neighbors (KNN) and Decision Trees (DT). A comparative analysis between two ensemble classifiers and two individual classifiers is conducted, revealing the superior performance of the ensemble classifiers. Our investigation utilizes four distinct cancer gene expression datasets to showcase the efficacy of employing ensemble classifiers and gene selection methods for cancer classification. The ensemble classifier (Bagging Classifier), in conjunction with the MRMR method selecting only the top 30 genes, achieved an impressive overall accuracy of 94% across all four employed datasets.

A comparison of marker gene selection methods for single-cell RNA sequencing data

BackgroundThe development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalog and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper, we benchmark 59 computational methods for selecting marker genes in scRNA-seq data.ResultsWe compare the performance of the methods using 14 real scRNA-seq datasets and over 170 additional simulated datasets. Methods are compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed, and their implementation quality. In addition, various case studies are used to scrutinize the most commonly used methods, highlighting issues and inconsistencies.ConclusionsOverall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student’s t-test, and logistic regression.

Machine Learning Methods for Gene Selection in Uveal Melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.

Gene panel selection for targeted spatial transcriptomics

Targeted spatial transcriptomics hold particular promise in analyzing complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is their reliance on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method performing gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network.

Introduction: Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine. It enables the identification of the most effective drugs based on individual genetic profiles, aids in selecting potential drug candidates, and helps identify biomarkers that predict drug efficacy and toxicity.A deeper investigation on drug response prediction reveals that drugs exert their effects by targeting specific proteins, which in turn perturb related genes in cascading ways. This perturbation affects cellular pathways and regulatory networks, ultimately influencing the cellular response to the drug. Identifying which genes are perturbed and how they interact can provide critical insights into the mechanisms of drug action. Hence, the problem of predicting drug response can be framed as a dual problem involving both the prediction of drug efficacy and the selection of drug-specific genes. Identifying these drug-specific genes (biomarkers) is crucial because they serve as indicators of how the drug will affect the biological system, thereby facilitating both drug response prediction and biomarker discovery.Methods: In this study, we propose DGDRP (Drug-specific Gene selection for Drug Response Prediction), a graph neural network (GNN)-based model that uses a novel rank-and-re-rank process for drug-specific gene selection. DGDRP first ranks genes using a pathway knowledge-enhanced network propagation algorithm based on drug target information, ensuring biological relevance. It then re-ranks genes based on the similarity between gene and drug target embeddings learned from the GNN, incorporating semantic relationships. Thus, our model adaptively learns to select drug mechanism-associated genes that contribute to drug response prediction. This integrated approach not only improves drug response predictions compared to other gene selection methods but also allows for effective biomarker discovery.Discussion: As a result, our approach demonstrates improved drug response predictions compared to other gene selection methods and demonstrates comparability with state-of-the-art deep learning models. Case studies further support our method by showing alignment of selected gene sets with the mechanisms of action of input drugs.Conclusion: Overall, DGDRP represents a deep learning based re-ranking strategy, offering a robust gene selection framework for more accurate drug response prediction. The source code for DGDRP can be found at: https://github.com/minwoopak/heteronet.

Integrating gene selection and deep learning for enhanced Autisms' disease prediction: a comparative study using microarray data

&lt;abstract&gt; &lt;p&gt;In this article, Autism Spectrum Disorder (ASD) is discussed, with an emphasis placed on the multidimensional nature of the disorder, which is anchored in genetic and neurological components. Identifying genes related to ASD is essential to comprehend the mechanisms that underlie the illness, yet the condition's complexity has impeded precise information in this field. In ASD research, the analysis of gene expression data helps choose and categorize significant genes. The study used microarray data to provide a novel approach that integrated gene selection techniques with deep learning models to improve the accuracy of ASD prediction. It offered a detailed comparative examination of gene selection approaches and deep learning architectures, including singular value decompositions (SVD), principal component analyses (PCA), and convolutional neural networks (CNNs). This paper combines gene selection methods (PCA and SVD) with deep learning models (CNN) to improve ASD prediction. Compared to more traditional approaches, the study revealed that its integrated methodology was more effective in improving the accuracy of ASD prediction results through experimentation. There was a difference in the accuracy between the PCA-CNN model, which achieved 94.33% with a loss of 0.4312, and the SVD-CNN model, which achieved 92.21% with a loss less than or equal to 0.3354. These discoveries help in the development of more accurate diagnostic and prognostic tools for ASD, which is a complicated neurodevelopmental disorder. Additionally, they provide insights into the molecular pathways that underlie ASD.&lt;/p&gt; &lt;/abstract&gt;

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm