{"id":"https://openalex.org/W2962876161","doi":"https://doi.org/10.24963/ijcai.2018/483","title":"Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders","display_name":"Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2962876161","doi":"https://doi.org/10.24963/ijcai.2018/483","mag":"2962876161"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2018/483","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/483","pdf_url":"https://www.ijcai.org/proceedings/2018/0483.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2018/0483.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086690079","display_name":"Tengfei Ma","orcid":"https://orcid.org/0000-0002-1086-529X"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tengfei Ma","raw_affiliation_strings":["IBM Research","MIT-IBM Watson AI Lab","IBM Research; MIT-IBM Watson AI Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]},{"raw_affiliation_string":"MIT-IBM Watson AI Lab","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM Research; MIT-IBM Watson AI Lab","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100645991","display_name":"Cao Xiao","orcid":"https://orcid.org/0000-0002-3869-6942"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cao Xiao","raw_affiliation_strings":["IBM Research","MIT-IBM Watson AI Lab","IBM Research; MIT-IBM Watson AI Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]},{"raw_affiliation_string":"MIT-IBM Watson AI Lab","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM Research; MIT-IBM Watson AI Lab","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047215778","display_name":"Jiayu Zhou","orcid":"https://orcid.org/0000-0003-4336-6777"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiayu Zhou","raw_affiliation_strings":["Computer Science and Engineering, Michigan State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Michigan State University","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455796","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0002-1759-2570"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Weill Cornell Medical School, Cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weill Cornell Medical School, Cornell University","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":201,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3477","last_page":"3483"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9855999946594238,"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/T10375","display_name":"Pharmacogenetics and Drug Metabolism","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/3004","display_name":"Pharmacology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9485124349594116},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7142502069473267},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7092211246490479},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5985938310623169},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.578144907951355},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5351786017417908},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5148279070854187},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4594079256057739},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3611794710159302},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.19859075546264648}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9485124349594116},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7142502069473267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7092211246490479},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5985938310623169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.578144907951355},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5351786017417908},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5148279070854187},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4594079256057739},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3611794710159302},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.19859075546264648},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2018/483","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/483","pdf_url":"https://www.ijcai.org/proceedings/2018/0483.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2018/483","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/483","pdf_url":"https://www.ijcai.org/proceedings/2018/0483.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1067560820","display_name":null,"funder_award_id":"IIS-1750326","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3232552192","display_name":"III: Small: Collaborative Research: Structured Methods for Multi-Task Learning","funder_award_id":"1615597","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3916121238","display_name":"CAREER: Interpretable Deep Modeling of Discrete Time Event Sequences","funder_award_id":"1750326","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4504108201","display_name":null,"funder_award_id":"N00014-17-1","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G5395595088","display_name":null,"funder_award_id":"N00014-17-1-2265","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G5794704315","display_name":null,"funder_award_id":"IIS-1716432","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6677749066","display_name":null,"funder_award_id":"IIS-1749940","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6832078411","display_name":"III: Small: Collaborative Research: Comprehensive Heterogeneous Response Regression from Complex Data","funder_award_id":"1716432","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7299422697","display_name":null,"funder_award_id":"NSF IIS-1750326","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7617836800","display_name":null,"funder_award_id":"IIS-1615597","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8858239272","display_name":"CAREER: Harness the Big Data via Large-Scale Lifelong Learning","funder_award_id":"1749940","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2962876161.pdf","grobid_xml":"https://content.openalex.org/works/W2962876161.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1018047830","https://openalex.org/W1161601223","https://openalex.org/W1968205511","https://openalex.org/W1969802403","https://openalex.org/W1977649864","https://openalex.org/W1987219048","https://openalex.org/W2009313526","https://openalex.org/W2034537627","https://openalex.org/W2068351024","https://openalex.org/W2106417713","https://openalex.org/W2118657370","https://openalex.org/W2119002393","https://openalex.org/W2135037015","https://openalex.org/W2136127280","https://openalex.org/W2137055149","https://openalex.org/W2145578524","https://openalex.org/W2153466147","https://openalex.org/W2292492942","https://openalex.org/W2322942874","https://openalex.org/W2488240688","https://openalex.org/W2570516417","https://openalex.org/W2604314403","https://openalex.org/W2605325415","https://openalex.org/W2964015378","https://openalex.org/W3000384245","https://openalex.org/W4322614756"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W2892165056","https://openalex.org/W2098964748"],"abstract_inverted_index":{"Drug":[0],"similarity":[1,45,69,95],"has":[2,136],"been":[3],"studied":[4],"to":[5,59,66,90,117,126],"support":[6],"downstream":[7],"clinical":[8],"tasks":[9,128],"such":[10],"as":[11],"inferring":[12],"novel":[13],"properties":[14],"of":[15,28,31,37,51,100],"drugs":[16],"(e.g.":[17,159],"side":[18],"effects,":[19],"indications,":[20],"interactions)":[21],"from":[22,97],"known":[23],"properties.":[24],"The":[25],"growing":[26],"availability":[27],"new":[29],"types":[30,99],"drug":[32,44,53,101],"features":[33,130],"brings":[34],"the":[35,48,107,119],"opportunity":[36],"learning":[38],"a":[39,79],"more":[40],"comprehensive":[41],"and":[42,83,93,113,129,142,163],"accurate":[43,68,92],"that":[46],"represents":[47],"full":[49],"spectrum":[50],"underlying":[52],"relations.":[54],"However,":[55],"it":[56],"is":[57,78],"challenging":[58],"integrate":[60],"these":[61],"heterogeneous,":[62],"noisy,":[63],"nonlinear-related":[64],"information":[65],"learn":[67,91],"measures":[70,96],"especially":[71],"when":[72],"labels":[73],"are":[74],"scarce.":[75],"Moreover,":[76],"there":[77],"trade-off":[80],"between":[81],"accuracy":[82,150],"interpretability.":[84,133,164],"In":[85,103],"this":[86],"paper,":[87],"we":[88,105],"propose":[89],"interpretable":[94],"multiple":[98],"features.":[102],"particular,":[104],"model":[106,135,157],"integration":[108],"using":[109],"multi-view":[110],"graph":[111],"auto-encoders,":[112],"add":[114],"attentive":[115],"mechanism":[116],"determine":[118],"weights":[120],"for":[121,131,139],"each":[122],"view":[123],"with":[124],"respect":[125],"corresponding":[127],"better":[132,156],"Our":[134],"flexible":[137],"design":[138],"both":[140],"semi-supervised":[141],"unsupervised":[143],"settings.":[144],"Experimental":[145],"results":[146],"demonstrated":[147],"significant":[148],"predictive":[149],"improvement.":[151],"Case":[152],"studies":[153],"also":[154],"showed":[155],"capacity":[158],"embed":[160],"node":[161],"features)":[162]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":23},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":27},{"year":2021,"cited_by_count":51},{"year":2020,"cited_by_count":25},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":5}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
