{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T15:32:37Z","timestamp":1785857557069,"version":"3.56.0"},"reference-count":58,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T00:00:00Z","timestamp":1748390400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The analysis of high-dimensional microarray gene expression data presents critical challenges, including excessive dimensionality, increased computational burden, and sensitivity to random initialization. Traditional optimization algorithms often produce inconsistent and suboptimal results, while failing to preserve local data structures limiting both predictive accuracy and biological interpretability. To address these limitations, this study proposes an adaptive neighborhood-preserving multi-objective particle swarm optimization (ANPMOPSO) framework for gene selection. ANPMOPSO introduces four key innovations: (1) a weighted neighborhood-preserving ensemble embedding (WNPEE) technique for dimensionality reduction that retains local structure; (2) Sobol sequence (SS) initialization to enhance population diversity and convergence stability; (3) a differential evolution (DE)-based adaptive velocity update to dynamically balance exploration and exploitation; and (4) a novel ranking strategy that combines Pareto dominance with neighborhood preservation quality to prioritize biologically meaningful gene subsets. Experimental evaluations on six benchmark microarray datasets and eleven multi-modal test functions (MMFs) demonstrate that ANPMOPSO consistently outperforms state-of-the-art methods. For example, it achieves 100% classification accuracy on Leukemia and Small-Round-Blue-Cell Tumor (SRBCT) using only 3\u20135 genes, improving accuracy by 5\u201315% over competitors while reducing gene subsets by 40\u201360%. Additionally, on MMFs, ANPMOPSO attains superior hypervolume values (<jats:italic>e.g<\/jats:italic>., 1.0617 \u00b1 0.2225 on MMF1, approximately 10\u201320% higher than competitors), confirming its robustness in balancing convergence and diversity. Although the method incurs higher training time due to its structural and adaptive components, it achieves a strong trade-off between computational cost and biological relevance, making it a promising tool for high-dimensional gene selection in bioinformatics.<\/jats:p>","DOI":"10.7717\/peerj-cs.2872","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T08:24:36Z","timestamp":1748420676000},"page":"e2872","source":"Crossref","is-referenced-by-count":3,"title":["Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization"],"prefix":"10.7717","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7928-8662","authenticated-orcid":true,"given":"Sumet","family":"Mehta","sequence":"first","affiliation":[{"name":"School of Computer Science & Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China"},{"name":"R&D, Star Engineers India Pvt. Ltd., Pune, Maharashtra, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science & Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7234-8108","authenticated-orcid":true,"given":"Muhammad","family":"Sohail","sequence":"additional","affiliation":[{"name":"Department of Computer Software Engineering, Military College of Signals, NUST, Islamabad, Islamabad, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3452-9581","authenticated-orcid":true,"given":"Bhekisipho","family":"Twala","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria West, Pretoria, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asad","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Computer Software Engineering, Military College of Signals, NUST, Islamabad, Islamabad, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fasee","family":"Ullah","sequence":"additional","affiliation":[{"name":"The Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, Perak Darul Ridzuan, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arfat Ahmad","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen, Khon Kaen, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Jiangsu University of Science & Technology, Zhenjiang, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2025,5,28]]},"reference":[{"issue":"19","key":"10.7717\/peerj-cs.2872\/ref-1","first-page":"4759","article-title":"Application of complementary DNA microarray technology to carcinogen identification, toxicology, and drug safety evaluation","volume":"59","author":"Afshari","year":"1999","journal-title":"Cancer Research"},{"key":"10.7717\/peerj-cs.2872\/ref-2","first-page":"646","article-title":"On the use of Sobol\u2019 sequence for high dimensional simulation","author":"Atanassov","year":"2022"},{"issue":"1","key":"10.7717\/peerj-cs.2872\/ref-3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01696-3","article-title":"A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm","volume":"21","author":"Azadifar","year":"2021","journal-title":"BMC Medical Informatics and Decision Making"},{"issue":"22","key":"10.7717\/peerj-cs.2872\/ref-4","doi-asserted-by":"publisher","first-page":"12179","DOI":"10.1007\/s00500-022-07032-9","article-title":"Application of nature inspired soft computing techniques for gene selection: a novel frame work for classification of cancer","volume":"26","author":"Aziz","year":"2022","journal-title":"Soft Computing"},{"key":"10.7717\/peerj-cs.2872\/ref-5","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/j.asoc.2014.08.032","article-title":"Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data","volume":"24","author":"Chen","year":"2014","journal-title":"Applied Soft Computing"},{"key":"10.7717\/peerj-cs.2872\/ref-6","first-page":"635","article-title":"Reference point based multi-objective optimization using evolutionary algorithms","author":"Deb","year":"2006"},{"key":"10.7717\/peerj-cs.2872\/ref-7","first-page":"47","article-title":"Omni-optimizer: a procedure for single and multi-objective optimization","author":"Deb","year":"2005"},{"issue":"1","key":"10.7717\/peerj-cs.2872\/ref-8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-023-50600-8","article-title":"Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine","volume":"14","author":"DeGroat","year":"2024","journal-title":"Scientific Reports"},{"issue":"Pt A","key":"10.7717\/peerj-cs.2872\/ref-9","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.envpol.2018.06.051","article-title":"Short-term transcriptome and microRNAs responses to exposure to different air pollutants in two population studies","volume":"242","author":"Esp\u00edn-P\u00e9rez","year":"2018","journal-title":"Environmental Pollution"},{"key":"10.7717\/peerj-cs.2872\/ref-10","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1007\/s00500-010-0597-8","article-title":"Simulated annealing for supervised gene selection","volume":"15","author":"Filippone","year":"2011","journal-title":"Soft Computing"},{"key":"10.7717\/peerj-cs.2872\/ref-11","doi-asserted-by":"publisher","first-page":"120483","DOI":"10.1016\/j.ins.2024.120483","article-title":"MOFS-REPLS: a large-scale multi-objective feature selection algorithm based on real-valued encoding and preference leadership strategy","volume":"667","author":"Fu","year":"2024","journal-title":"Information Sciences"},{"issue":"1","key":"10.7717\/peerj-cs.2872\/ref-12","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1186\/s13073-021-01001-x","article-title":"Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data","volume":"13","author":"Fustero-Torre","year":"2021","journal-title":"Genome Medicine"},{"key":"10.7717\/peerj-cs.2872\/ref-13","doi-asserted-by":"publisher","first-page":"111380","DOI":"10.1016\/j.knosys.2024.111380","article-title":"Information gain ratio-based subfeature grouping empowers particle swarm optimization for feature selection","volume":"286","author":"Gao","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"10.7717\/peerj-cs.2872\/ref-14","doi-asserted-by":"publisher","first-page":"100847","DOI":"10.1016\/j.swevo.2021.100847","article-title":"Multi-objective particle swarm optimization with adaptive strategies for feature selection","volume":"62","author":"Han","year":"2021","journal-title":"Swarm and Evolutionary Computation"},{"issue":"2","key":"10.7717\/peerj-cs.2872\/ref-15","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1007\/s11063-024-11553-9","article-title":"A feature selection method based on feature-label correlation information and self-adaptive MOPSO","volume":"56","author":"Han","year":"2024","journal-title":"Neural Processing Letters"},{"issue":"5","key":"10.7717\/peerj-cs.2872\/ref-16","doi-asserted-by":"publisher","first-page":"e97530","DOI":"10.1371\/journal.pone.0097530","article-title":"A novel strategy for gene selection of microarray data based on gene-to-class sensitivity information","volume":"9","author":"Han","year":"2014","journal-title":"PLOS ONE"},{"issue":"8","key":"10.7717\/peerj-cs.2872\/ref-17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-2773-x","article-title":"A hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization","volume":"20","author":"Han","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"10.7717\/peerj-cs.2872\/ref-18","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TCBB.2015.2465906","article-title":"A gene selection method for microarray data based on binary PSO encoding gene-to-class sensitivity information","volume":"14","author":"Han","year":"2015","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"10.7717\/peerj-cs.2872\/ref-19","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1109\/TNANO.2024.3368628","article-title":"Efficient parallel stochastic computing multiply-accumulate (MAC) technique using Pseudo-Sobol bit-streams","volume":"23","author":"Hu","year":"2024","journal-title":"IEEE Transactions on Nanotechnology"},{"key":"10.7717\/peerj-cs.2872\/ref-20","first-page":"2419","article-title":"Evolutionary many-objective optimization: a short review. 2008 IEEE congress on evolutionary computation","author":"Ishibuchi","year":"2008"},{"key":"10.7717\/peerj-cs.2872\/ref-21","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.asoc.2017.09.038","article-title":"Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification","volume":"62","author":"Jain","year":"2018","journal-title":"Applied Soft Computing"},{"key":"10.7717\/peerj-cs.2872\/ref-22","first-page":"1942","article-title":"Particle swarm optimization","author":"Kennedy","year":"1995"},{"issue":"9","key":"10.7717\/peerj-cs.2872\/ref-23","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1016\/j.ress.2005.11.018","article-title":"Multi-objective optimization using genetic algorithms: a tutorial","volume":"91","author":"Konak","year":"2006","journal-title":"Reliability Engineering & System Safety"},{"key":"10.7717\/peerj-cs.2872\/ref-24","doi-asserted-by":"publisher","first-page":"110311","DOI":"10.1016\/j.asoc.2023.110311","article-title":"A dynamic dual-population co-evolution multi-objective evolutionary algorithm for constrained multi-objective optimization problems","volume":"141","author":"Kong","year":"2023","journal-title":"Applied Soft Computing"},{"key":"10.7717\/peerj-cs.2872\/ref-25","doi-asserted-by":"publisher","first-page":"110329","DOI":"10.1016\/j.asoc.2023.110329","article-title":"Parallel global best-worst particle swarm optimization algorithm for solving optimization problems","volume":"142","author":"Kumar","year":"2023","journal-title":"Applied Soft Computing"},{"key":"10.7717\/peerj-cs.2872\/ref-26","doi-asserted-by":"publisher","first-page":"106994","DOI":"10.1016\/j.asoc.2020.106994","article-title":"A gene selection algorithm using simplified swarm optimization with multi-filter ensemble technique","volume":"100","author":"Lai","year":"2021","journal-title":"Applied Soft Computing"},{"key":"10.7717\/peerj-cs.2872\/ref-27","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s00371-018-1582-y","article-title":"Learning a convolutional neural network for propagation-based stereo image segmentation","volume":"36","author":"Li","year":"2020","journal-title":"The Visual Computer"},{"key":"10.7717\/peerj-cs.2872\/ref-28","doi-asserted-by":"publisher","first-page":"109852","DOI":"10.1016\/j.asoc.2022.109852","article-title":"A particle swarm optimizer with dynamic balance of convergence and diversity for large-scale optimization","volume":"132","author":"Li","year":"2023","journal-title":"Applied Soft Computing"},{"key":"10.7717\/peerj-cs.2872\/ref-29","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1016\/j.asoc.2022.109852","article-title":"Gene selection using hybrid particle swarm optimization and genetic algorithm","volume":"12","author":"Li","year":"2008","journal-title":"Soft Computing"},{"key":"10.7717\/peerj-cs.2872\/ref-30","doi-asserted-by":"publisher","first-page":"101253","DOI":"10.1016\/j.swevo.2023.101253","article-title":"Multimodal multi-objective optimization: comparative study of the state-of-the-art","volume":"77","author":"Li","year":"2023","journal-title":"Swarm and Evolutionary Computation"},{"issue":"4","key":"10.7717\/peerj-cs.2872\/ref-31","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1109\/TNB.2013.2294716","article-title":"Multi-objective binary biogeography based optimization for feature selection using gene expression data","volume":"12","author":"Li","year":"2013","journal-title":"IEEE Transactions on NanoBioscience"},{"key":"10.7717\/peerj-cs.2872\/ref-32","doi-asserted-by":"publisher","first-page":"101262","DOI":"10.1016\/j.swevo.2023.101262","article-title":"Cooperative coevolutionary multi-guide particle swarm optimization algorithm for large-scale multi-objective optimization problems","volume":"78","author":"Madani","year":"2023","journal-title":"Swarm and Evolutionary Computation"},{"key":"10.7717\/peerj-cs.2872\/ref-33","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2025.3526825","article-title":"MORPSO_ECD+ ELM: a unified framework for gene selection and cancer classification","author":"Mehta","year":"2025","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"2","key":"10.7717\/peerj-cs.2872\/ref-34","doi-asserted-by":"publisher","first-page":"219","DOI":"10.3390\/electronics8020219","article-title":"Weighted neighborhood preserving ensemble embedding","volume":"8","author":"Mehta","year":"2019","journal-title":"Electronics"},{"key":"10.7717\/peerj-cs.2872\/ref-35","doi-asserted-by":"publisher","first-page":"8184367","DOI":"10.1155\/2023\/8184367","article-title":"Multi-objective service composition using enhanced multi-objective differential evolution algorithm","volume":"1","author":"Peng","year":"2023","journal-title":"Computational Intelligence and Neuroscience"},{"key":"10.7717\/peerj-cs.2872\/ref-36","doi-asserted-by":"publisher","first-page":"6361","DOI":"10.1109\/TMM.2023.3349075","article-title":"Deep neighborhood-preserving hashing with quadratic spherical mutual information for cross-modal retrieval","volume":"26","author":"Qin","year":"2024","journal-title":"IEEE Transactions on Multimedia"},{"issue":"16","key":"10.7717\/peerj-cs.2872\/ref-37","doi-asserted-by":"publisher","first-page":"15171","DOI":"10.1007\/s00432-023-05308-7","article-title":"Classification of cancer cells and gene selection based on microarray data using MOPSO algorithm","volume":"149","author":"Rahimi","year":"2023","journal-title":"Journal of Cancer Research and Clinical Oncology"},{"issue":"6","key":"10.7717\/peerj-cs.2872\/ref-38","doi-asserted-by":"publisher","first-page":"4370","DOI":"10.1016\/j.ygeno.2020.07.027","article-title":"Integration of multi-objective PSO based feature selection and node centrality for medical datasets","volume":"112","author":"Rostami","year":"2020","journal-title":"Genomics"},{"issue":"1","key":"10.7717\/peerj-cs.2872\/ref-39","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1038\/gim.2016.69","article-title":"Comprehensive genetic analysis of pregnancy loss by chromosomal microarrays: outcomes, benefits, and challenges","volume":"19","author":"Sahoo","year":"2017","journal-title":"Genetics in Medicine"},{"key":"10.7717\/peerj-cs.2872\/ref-40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-020-05367-8","article-title":"Optimized gene selection and classification of cancer from microarray gene expression data using deep learning","volume":"2020","author":"Shah","year":"2020","journal-title":"Neural Computing and Applications"},{"key":"10.7717\/peerj-cs.2872\/ref-41","doi-asserted-by":"publisher","first-page":"100661","DOI":"10.1016\/j.swevo.2020.100661","article-title":"Gene selection for cancer types classification using novel hybrid metaheuristics approach","volume":"54","author":"Shukla","year":"2020","journal-title":"Swarm and Evolutionary Computation"},{"issue":"3","key":"10.7717\/peerj-cs.2872\/ref-42","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1162\/evco.2009.17.3.411","article-title":"A preference-based evolutionary algorithm for multi-objective optimization","volume":"17","author":"Thiele","year":"2009","journal-title":"Evolutionary Computation"},{"key":"10.7717\/peerj-cs.2872\/ref-43","doi-asserted-by":"publisher","first-page":"123337","DOI":"10.1016\/j.eswa.2024.123337","article-title":"An enhanced particle swarm optimization with position update for optimal feature selection","volume":"247","author":"Tijjani","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.7717\/peerj-cs.2872\/ref-44","doi-asserted-by":"publisher","first-page":"117100","DOI":"10.1016\/j.measurement.2025.117100","article-title":"Multi-scale three-path network (MSTP-Net): a new architecture for retinal vessel segmentation","volume":"250","author":"Wang","year":"2025","journal-title":"Measurement"},{"issue":"6","key":"10.7717\/peerj-cs.2872\/ref-45","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.1109\/TCBB.2020.2974953","article-title":"Evolving multi-objective cancer subtype diagnosis from cancer gene expression data","volume":"18","author":"Wang","year":"2020","journal-title":"IEEE\/ACM transactions on Computational Biology and Bioinformatics"},{"issue":"3","key":"10.7717\/peerj-cs.2872\/ref-46","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1017\/S0007114517003506","article-title":"Statistical analysis of human microarray data shows that dietary intervention with n-3 fatty acids, flavonoids and resveratrol enriches for immune response and disease pathways","volume":"119","author":"Warburton","year":"2018","journal-title":"British Journal of Nutrition"},{"key":"10.7717\/peerj-cs.2872\/ref-47","doi-asserted-by":"publisher","first-page":"1191378","DOI":"10.3389\/fnagi.2023.1191378","article-title":"The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson\u2019s disease","volume":"15","author":"Wu","year":"2023","journal-title":"Frontiers in Aging Neuroscience"},{"issue":"1","key":"10.7717\/peerj-cs.2872\/ref-48","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/ht8010004","article-title":"A selective review of multi-level omics data integration using variable selection","volume":"8","author":"Wu","year":"2019","journal-title":"High Throughput"},{"key":"10.7717\/peerj-cs.2872\/ref-49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3228-0","article-title":"An efficient gene selection method for microarray data based on LASSO and BPSO","volume":"20","author":"Xiong","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"10.7717\/peerj-cs.2872\/ref-50","doi-asserted-by":"publisher","first-page":"120086","DOI":"10.1016\/j.renene.2024.120086","article-title":"Multi-objective particle swarm optimization algorithm based on multi-strategy improvement for hybrid energy storage optimization configuration","volume":"223","author":"Xu","year":"2024","journal-title":"Renewable Energy"},{"issue":"6","key":"10.7717\/peerj-cs.2872\/ref-51","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","article-title":"Particle swarm optimization for feature selection in classification: a multi-objective approach","volume":"43","author":"Xue","year":"2012","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.7717\/peerj-cs.2872\/ref-52","doi-asserted-by":"publisher","first-page":"5132803","DOI":"10.1155\/2020\/5132803","article-title":"Multi\/many-objective particle swarm optimization algorithm based on competition mechanism","volume":"1","author":"Yang","year":"2020","journal-title":"Computational Intelligence and Neuroscience"},{"key":"10.7717\/peerj-cs.2872\/ref-53","doi-asserted-by":"publisher","first-page":"107987","DOI":"10.1016\/j.cmpb.2023.107987","article-title":"An improved binary particle swarm optimization algorithm for clinical cancer biomarker identification in microarray data","volume":"244","author":"Yang","year":"2024","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"5","key":"10.7717\/peerj-cs.2872\/ref-54","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1109\/TEVC.2017.2754271","article-title":"A multi-objective particle swarm optimizer using ring topology for solving multimodal multi-objective problems","volume":"22","author":"Yue","year":"2017","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"6","key":"10.7717\/peerj-cs.2872\/ref-55","doi-asserted-by":"publisher","first-page":"8542","DOI":"10.1109\/TVT.2024.3361454","article-title":"An improved snake optimization algorithm for sparse conformal array beamforming","volume":"73","author":"Zhang","year":"2024","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"6","key":"10.7717\/peerj-cs.2872\/ref-56","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.1109\/TEVC.2017.2754271","article-title":"Network biomarker detection from gene co-expression network using Gaussian mixture model clustering","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"10.7717\/peerj-cs.2872\/ref-57","doi-asserted-by":"publisher","first-page":"102361","DOI":"10.1016\/j.inffus.2024.102361","article-title":"A review of cancer data fusion methods based on deep learning","volume":"108","author":"Zhao","year":"2024","journal-title":"Information Fusion"},{"key":"10.7717\/peerj-cs.2872\/ref-58","doi-asserted-by":"publisher","first-page":"103042","DOI":"10.1016\/j.artmed.2024.103042","article-title":"Rough hypervolume-driven feature selection with groupwise intelligent sampling for detecting clinical characterization of lupus nephritis","volume":"160","author":"Zhou","year":"2025","journal-title":"Artificial Intelligence in Medicine"}],"container-title":["PeerJ Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/peerj.com\/articles\/cs-2872.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-2872.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-2872.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-2872.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T08:24:41Z","timestamp":1748420681000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/cs-2872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,28]]},"references-count":58,"alternative-id":["10.7717\/peerj-cs.2872"],"URL":"https:\/\/doi.org\/10.7717\/peerj-cs.2872","archive":["CLOCKSS","LOCKSS","Portico"],"relation":{},"ISSN":["2376-5992"],"issn-type":[{"value":"2376-5992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,28]]},"article-number":"e2872"}}