{"id":"https://openalex.org/W7171570997","doi":"https://doi.org/10.1145/3807503.3819365","title":"From Genes to Subtypes: Benchmarking Feature Selection in Glioblastoma","display_name":"From Genes to Subtypes: Benchmarking Feature Selection in Glioblastoma","publication_year":2026,"publication_date":"2026-06-30","ids":{"openalex":"https://openalex.org/W7171570997","doi":"https://doi.org/10.1145/3807503.3819365"},"language":null,"primary_location":{"id":"doi:10.1145/3807503.3819365","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3807503.3819365","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3807503.3819365","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063812766","display_name":"Kim Anh Phung","orcid":"https://orcid.org/0000-0002-4561-7536"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kim Anh Phung","raw_affiliation_strings":["Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA"],"raw_orcid":"https://orcid.org/0000-0002-4561-7536","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA","institution_ids":["https://openalex.org/I63135867"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101544978","display_name":"Justin Zhan","orcid":"https://orcid.org/0000-0001-5458-8282"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Justin Zhan","raw_affiliation_strings":["Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA"],"raw_orcid":"https://orcid.org/0000-0001-5458-8282","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA","institution_ids":["https://openalex.org/I63135867"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I63135867"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/glioblastoma","display_name":"Glioblastoma","score":0.5957000255584717},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5623999834060669},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.546500027179718},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5325000286102295},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4629000127315521},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.4032000005245209},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36660000681877136}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6082000136375427},{"id":"https://openalex.org/C2776194525","wikidata":"https://www.wikidata.org/wiki/Q282142","display_name":"Glioblastoma","level":2,"score":0.5957000255584717},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5623999834060669},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5590999722480774},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5325000286102295},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4629000127315521},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.397599995136261},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29170000553131104},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.27489998936653137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3807503.3819365","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3807503.3819365","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3807503.3819365","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3807503.3819365","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4841390714","display_name":null,"funder_award_id":"N00014-23-1-2396","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G5479835431","display_name":"Beginnings: Establishing an Experiential Learning Pathway in Artificial Intelligence","funder_award_id":"2346609","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5770373782","display_name":"The CSGrad4US Fellowship Program","funder_award_id":"2313998","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8150659101","display_name":"Proto-OKN Theme 1: The Water-Energy Nexus Open Knowledge Network (WEN-OKN)","funder_award_id":"2333726","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8776343857","display_name":"Southwest Ohio Scholars: (Re)defining Academic Belonging for Computer Science Students and Families","funder_award_id":"2322109","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W54887220","https://openalex.org/W1505191356","https://openalex.org/W1677182931","https://openalex.org/W1979996936","https://openalex.org/W2025183726","https://openalex.org/W2028236537","https://openalex.org/W2096192437","https://openalex.org/W2119387367","https://openalex.org/W2132886470","https://openalex.org/W2154053567","https://openalex.org/W2161289668","https://openalex.org/W2961217357","https://openalex.org/W2975379509","https://openalex.org/W4200327950","https://openalex.org/W4230962320","https://openalex.org/W4297382430","https://openalex.org/W4300430800","https://openalex.org/W4384818983","https://openalex.org/W4393488395","https://openalex.org/W4405459516","https://openalex.org/W4406069000","https://openalex.org/W4410242339","https://openalex.org/W4416734703"],"related_works":[],"abstract_inverted_index":{"Glioblastoma":[0],"(GBM)":[1],"is":[2,86,128],"a":[3,14,32,49,61,116],"highly":[4],"heterogeneous":[5],"brain":[6],"tumor,":[7],"and":[8,57,157,168,196,201],"accurate":[9],"subtype":[10],"prediction":[11],"requires":[12],"identifying":[13],"compact":[15],"yet":[16],"informative":[17],"set":[18],"of":[19,22,48,55,97],"genes":[20],"capable":[21],"distinguishing":[23],"molecular":[24],"profiles.":[25],"In":[26],"this":[27],"paper,":[28],"we":[29],"propose":[30],"GBMNet,":[31],"feature":[33,68,126],"selection":[34,69,127],"framework":[35],"that":[36,71,178],"integrates":[37],"representation":[38],"learning":[39],"with":[40,121,193],"\u21131-regularized":[41],"sparsity":[42],"directly":[43],"at":[44,205],"the":[45,90,182,194],"input":[46],"layer":[47],"feed-forward":[50],"network,":[51],"enabling":[52],"joint":[53],"optimization":[54],"classification":[56],"gene":[58,104,172,207],"ranking":[59],"in":[60],"single":[62],"task-driven":[63],"objective.":[64],"Unlike":[65],"prior":[66],"neural":[67],"methods":[70],"rely":[72],"on":[73],"post-hoc":[74],"importance":[75],"estimation":[76],"or":[77],"require":[78],"large-scale":[79],"data":[80],"to":[81,134],"stabilize":[82],"attention":[83],"weights,":[84],"GBMNet":[85,109,179],"explicitly":[87],"designed":[88],"for":[89,189],"extreme":[91],"p":[92],"\u226b":[93],"n":[94],"regime":[95],"characteristic":[96],"GBM":[98],"transcriptomics,":[99],"producing":[100],"intrinsically":[101],"interpretable,":[102],"model-consistent":[103],"rankings.":[105],"We":[106],"systematically":[107],"benchmark":[108],"against":[110],"three":[111],"widely":[112],"used":[113],"baselines":[114],"under":[115],"unified,":[117],"leak-free":[118],"evaluation":[119],"protocol":[120],"stratified":[122],"5-fold":[123],"cross-validation,":[124],"where":[125],"performed":[129],"within":[130],"each":[131],"training":[132],"fold":[133],"avoid":[135],"information":[136],"leakage.":[137],"Six":[138],"classifiers":[139],"including":[140],"Nearest":[141],"Neighbor":[142],"(NN),":[143],"K-Nearest":[144],"Neighbors":[145],"(KNN),":[146],"Support":[147],"Vector":[148],"Machines":[149],"(SVM),":[150],"Random":[151],"Forests":[152],"(RF),":[153],"Multilayer":[154],"Perceptron":[155],"(MLP),":[156],"Convolutional":[158],"Neural":[159],"Networks":[160],"(CNN)":[161],"are":[162],"evaluated":[163],"using":[164],"accuracy,":[165],"precision,":[166],"recall,":[167],"F1-score":[169],"across":[170,185],"eight":[171],"subset":[173],"sizes.":[174],"Experimental":[175],"results":[176],"demonstrate":[177],"consistently":[180],"achieves":[181],"highest":[183],"performance":[184],"all":[186],"four":[187],"metrics":[188],"most":[190],"classifier\u2013subset":[191],"combinations,":[192],"SVM\u2013GBMNet":[195],"MLP\u2013GBMNet":[197],"pairings":[198],"reaching":[199],"0.94":[200],"0.95":[202],"accuracy":[203],"respectively":[204],"moderate":[206],"panel":[208],"sizes":[209],"(300\u2013500":[210],"genes).":[211]},"counts_by_year":[],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2026-07-29T00:00:00"}