{"id":"https://openalex.org/W3192157621","doi":"https://doi.org/10.1145/3459930.3469542","title":"Cancer molecular subtype classification by graph convolutional networks on multi-omics data","display_name":"Cancer molecular subtype classification by graph convolutional networks on multi-omics data","publication_year":2021,"publication_date":"2021-07-30","ids":{"openalex":"https://openalex.org/W3192157621","doi":"https://doi.org/10.1145/3459930.3469542","mag":"3192157621"},"language":"en","primary_location":{"id":"doi:10.1145/3459930.3469542","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459930.3469542","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101530082","display_name":"Bingjun Li","orcid":"https://orcid.org/0000-0003-0513-3497"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bingjun Li","raw_affiliation_strings":["University of Connecticut"],"affiliations":[{"raw_affiliation_string":"University of Connecticut","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100417444","display_name":"Tianyu Wang","orcid":"https://orcid.org/0000-0003-4169-8268"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianyu Wang","raw_affiliation_strings":["University of Connecticut"],"affiliations":[{"raw_affiliation_string":"University of Connecticut","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010254642","display_name":"Sheida Nabavi","orcid":"https://orcid.org/0000-0002-5996-1020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheida Nabavi","raw_affiliation_strings":["University of Connecticut"],"affiliations":[{"raw_affiliation_string":"University of Connecticut","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101530082"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3014,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.80093475,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11297","display_name":"Ferroptosis and cancer prognosis","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6507202386856079},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6426887512207031},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6165162920951843},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5624076724052429},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5451459288597107},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5240000486373901},{"id":"https://openalex.org/keywords/biological-network","display_name":"Biological network","score":0.5059825778007507},{"id":"https://openalex.org/keywords/graph-database","display_name":"Graph database","score":0.4465920627117157},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4021153450012207},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.38466721773147583},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.23496487736701965},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.15752995014190674}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6507202386856079},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6426887512207031},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6165162920951843},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5624076724052429},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5451459288597107},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5240000486373901},{"id":"https://openalex.org/C28225019","wikidata":"https://www.wikidata.org/wiki/Q4915005","display_name":"Biological network","level":2,"score":0.5059825778007507},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.4465920627117157},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4021153450012207},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.38466721773147583},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.23496487736701965},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.15752995014190674}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459930.3469542","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459930.3469542","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.5299999713897705}],"awards":[{"id":"https://openalex.org/G7738114373","display_name":null,"funder_award_id":"1942303","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1967220865","https://openalex.org/W2067428007","https://openalex.org/W2158787690","https://openalex.org/W2817817898","https://openalex.org/W2900569176","https://openalex.org/W2967844557","https://openalex.org/W3028304854","https://openalex.org/W3093053585","https://openalex.org/W3100495486","https://openalex.org/W3102979309","https://openalex.org/W3119005666","https://openalex.org/W3168157106","https://openalex.org/W4200403811","https://openalex.org/W4245731973"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W4283723149","https://openalex.org/W31445736"],"abstract_inverted_index":{"Cancer":[0,32],"has":[1],"been":[2],"a":[3,24,39,70,78,109,115,142,156,269,275],"second":[4],"leading":[5],"cause":[6],"of":[7,19,51,67,69,105,163,193,198,203,210,216,228,233,259,262,292],"death":[8],"in":[9,214],"the":[10,90,134,152,161,174,191,201,204,208,211,226,249,253,260,290],"United":[11],"States":[12],"for":[13,27,59,282],"decades":[14],"and":[15,92,130,154,166,184,221,224,239],"an":[16],"accurate":[17],"classifier":[18],"cancers'":[20],"molecular":[21,45,99],"profiles":[22],"is":[23,280],"key":[25],"predictor":[26],"patients'":[28],"survival.":[29],"Recently":[30],"The":[31,245],"Genome":[33],"Atlas":[34],"research":[35],"networks":[36],"have":[37,55],"identified":[38],"new":[40],"cancer":[41,62,71,283],"taxonomy":[42],"based":[43],"on":[44,200],"tumor":[46],"subtypes":[47,68],"over":[48],"33":[49],"types":[50],"cancer.":[52],"Several":[53],"studies":[54],"reported":[56],"classification":[57,64,66,157],"models":[58,238,255],"traditional":[60],"tissue-of-origin":[61],"type":[63],"or":[65],"type.":[72],"In":[73],"this":[74],"study,":[75],"we":[76],"propose":[77],"novel":[79],"end-to-end":[80],"deep":[81,236],"learning":[82,237,243],"model":[83,91,103,213,230,251,265,287],"that":[84,113,159,248],"incorporates":[85],"prior":[86,120],"biological":[87],"knowledge":[88],"into":[89],"integrates":[93],"multi-omics":[94,131,294],"data":[95,132,295],"to":[96,137,147],"classify":[97],"pan-cancer":[98],"subtypes.":[100],"Our":[101,264,286],"proposed":[102,212,250],"consists":[104],"three":[106,234],"sections:":[107],"i)":[108],"graph":[110,125,176,195],"convolutional":[111],"network":[112,146],"takes":[114,160],"genet":[116],"interaction":[117,179,182],"network,":[118],"representing":[119],"knowledge,":[121],"as":[122,169],"its":[123],"input":[124,175,194],"where":[126],"genes":[127],"are":[128,133],"nodes":[129],"node":[135],"features,":[136],"extract":[138,148],"localized":[139,164],"features;":[140],"ii)":[141],"fully":[143],"connected":[144],"neural":[145],"global":[149,167],"features":[150,165,168],"from":[151],"data;":[153],"iii)":[155],"layer":[158],"combination":[162],"input.":[170],"We":[171,188,206],"examined":[172],"building":[173],"using":[177],"gene-gene":[178],"networks,":[180,183],"protein-protein":[181],"gene":[185],"co-expression":[186],"networks.":[187],"also":[189,274,288],"investigated":[190],"effect":[192],"size":[196],"(number":[197],"genes/nodes)":[199],"performance":[202,209,227],"model.":[205],"evaluated":[207],"terms":[215],"prediction":[217,271],"accuracy,":[218],"precision,":[219],"recall,":[220],"F1":[222],"score;":[223],"compared":[225,296],"our":[229],"with":[231,297],"those":[232],"state-of-the-art":[235],"two":[240],"conventional":[241],"machine":[242],"models.":[244],"results":[246],"show":[247],"outperforms":[252],"baseline":[254],"at":[256],"each":[257],"level":[258],"number":[261],"genes.":[263],"achieves":[266],"not":[267],"only":[268,299],"better":[270],"accuracy":[272],"but":[273],"lower":[276],"false-negative":[277],"rate,":[278],"which":[279],"important":[281],"patients":[284],"treatments.":[285],"shows":[289],"benefit":[291],"employing":[293,298],"single-omic":[300],"data.":[301]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}