[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-450463-105":59,"doc-detail-450463-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","discriminative-biomarker-selection-using-hybrid-multi-population-evolutionary-computation","Discriminative biomarker selection using hybrid multi-population evolutionary computation","","Proposes a hybrid multi-population evolutionary computation framework for discriminative biomarker selection and robust cancer classification from high-dimensional microarray gene data. Uses Kernel Principal Component Analysis (KPCA) to reduce dimensionality while retaining biologically meaningful patterns, then applies MultiPopulation Gravitational Search Algorithm (MPKGSA) with Opposition-Based Learning (OBL) to enhance exploration and avoid premature convergence. Evaluated on six microarray cancer datasets and a breast cancer SNP dataset, showing high prediction accuracy with minimal biomarker subsets and outperforming prior meta-heuristics.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/discriminative-biomarker-selection-using-hybrid-multi-population-evolutionary-computation/450463/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/discriminative-biomarker-selection-using-hybrid-multi-population-evolutionary-computation/450463.png","ImageObject",300,407,{"name":92,"@type":93},"Kyle","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-06","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What challenge does the method address in biomarker selection?","Question",{"text":112,"@type":113},"Conventional gene selection techniques struggle to find optimal biomarker subsets from high-dimensional, low-sample-size microarray data within feasible time.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed approach reduce gene data dimensionality?",{"text":117,"@type":113},"It uses Kernel Principal Component Analysis (KPCA) in the first stage to refine gene subsets while preserving biologically meaningful patterns.",{"name":119,"@type":110,"acceptedAnswer":120},"What role does Opposition-Based Learning (OBL) play in MPKGSA?",{"text":121,"@type":113},"OBL generates opposite solutions for each population and integrates them into the GSA update process, improving exploration diversity and reducing premature convergence.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},450463,1790825588,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},3985741905716,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nDiscriminative biomarker selection using hybrid multi-population evolutionary computation  \nAlok Kumar Shukla1, Shubhra Dwivedi1 & Aishwarya Mishra2  \nThe rapid advancement of Deoxyribonucleic acid (DNA) sequencing technology has gained more attention, especially in interpreting high-dimensional, low-sample-size microarray data for disease identification. However, conventional gene selection techniques struggle to identify optimal biomarker subsets from gene data within a feasible time. To address this, we propose a novel hybrid method for robust cancer classification and biomarker discovery. To reduce the dimensionality of gene data while preserving biologically meaningful patterns, in the first stage of our approach, Kernel Principal Component Analysis (KPCA) is utilized. The refined gene subsets are then processed by the MultiPopulation Gravitational Search Algorithm (GSA) known as MPKGSA with Opposition-Based Learning (OBL). The hybridization mechanism involves using OBL to generate a set of opposite solutions for each population, which is then integrated into the GSA update process. This process provides a more diverse exploration of the search space, preventing premature convergence on suboptimal gene subsets. The effectiveness of MPKGSA was evaluated on six microarray cancer datasets and a breast cancer single-nucleotide polymorphism (SNP) dataset from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) . Numerical results demonstrate that MPKGSA excels at balancing convergence and diversity, achieving high prediction accuracy with minimal biomarker subsets. Furthermore, it outperformed existing meta-heuristic methods, selecting a small number of gene biomarkers strongly correlated with the biological response class, confirming its utility for precise cancer identification and classification.  \nKeywords Minimum redundancy maximum relevance, Long short-term memory, Deep neural network, Convolution neural network, Intrusion detection  \nThe human genome is the full set of deoxyribonucleic acid sequence for humans. It consists approximately three billion base pairs in the double helix of DNA, more than 99% of them are the same among all populations, and less than 1% differ among individuals. The majority of DNA changes happen as Single Nucleotide Polymorphisms (SNPs). SNPs are the most important markers used for mapping diseases/cancers with genes. Over the past few years, microarray technology has been commonly used to measure the expression levels of thousands of genes simultaneously in a single experiment and analyze them to extract relevant genes to different areas of cancer types1. Gene expression profiles represent the abundance of messenger ribonucleic acid (mRNA) corresponding to specific genes2. Therefore, microarray technology has become a revolutionary tool for understanding human diseases. As a response, the rapid development and maturation of microarray technology allow researchers to measure the expression profiles of thousands of genes for discovering molecular disease biomarkers and aiding cancer diagnosis. To solve several issues like high-dimensionality, small sample size and noise, researchers have developed novel models that were effective and efficient for differentially expressed genes and for predicting the class of unknown samples3,4. The majority of high-dimensional gene expression data contains a significant amount of redundant genes, posing challenges for machine learning algorithms due to their high dimensionality. So, gene selection has been shown to be a successful method for improving performance by addressing several objectives, such as reducing the number of features and improving classification accuracy5.  \nIn the field of bioinformatics and precision medicine, high-dimensional data generated by high-throughput technologies can significantly impact medical diagnosis models6. Ac","cbCaikisLNpfezA8","https://ap.wps.com/l/cbCaikisLNpfezA8","pdf",5805006,26,"English","# Introduction\n# Related work and background\n# Problem motivation\n# Proposed method (KPCA and MPKGSA-OBL)","[{\"question\":\"What challenge does the method address in biomarker selection?\",\"answer\":\"Conventional gene selection techniques struggle to find optimal biomarker subsets from high-dimensional, low-sample-size microarray data within feasible time.\"},{\"question\":\"How does the proposed approach reduce gene data dimensionality?\",\"answer\":\"It uses Kernel Principal Component Analysis (KPCA) in the first stage to refine gene subsets while preserving biologically meaningful patterns.\"},{\"question\":\"What role does Opposition-Based Learning (OBL) play in MPKGSA?\",\"answer\":\"OBL generates opposite solutions for each population and integrates them into the GSA update process, improving exploration diversity and reducing premature convergence.\"}]","Discriminative biomarker selection using hybrid multi-population evolutionary computation | PDF",1790733264,66]