{"id":"https://openalex.org/W2070664771","doi":"https://doi.org/10.1145/2350176.2350178","title":"Globalized bipartite local model for drug-target interaction prediction","display_name":"Globalized bipartite local model for drug-target interaction prediction","publication_year":2012,"publication_date":"2012-08-12","ids":{"openalex":"https://openalex.org/W2070664771","doi":"https://doi.org/10.1145/2350176.2350178","mag":"2070664771"},"language":"en","primary_location":{"id":"doi:10.1145/2350176.2350178","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2350176.2350178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th International Workshop on Data Mining in Bioinformatics","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/A5046093778","display_name":"Jian-Ping Mei","orcid":"https://orcid.org/0000-0003-1678-6215"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Jian-Ping Mei","raw_affiliation_strings":["Nanyang Technological University, Singapore","Nanyang Technological University (Singapore)"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Nanyang Technological University (Singapore)","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112553040","display_name":"Chee-Keong Kwoh","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Chee-Keong Kwoh","raw_affiliation_strings":["Nanyang Technological University, Singapore","Nanyang Technological University (Singapore)"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Nanyang Technological University (Singapore)","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101863403","display_name":"Peng Yang","orcid":"https://orcid.org/0009-0005-3449-5843"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Peng Yang","raw_affiliation_strings":["Nanyang Technological University, Singapore","Nanyang Technological University (Singapore)"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Nanyang Technological University (Singapore)","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418684","display_name":"Xiaoli Li","orcid":"https://orcid.org/0000-0002-0762-6562"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Xiao-Li Li","raw_affiliation_strings":["Institute for Infocomm Research, Connexis, Singapore","[Institute for Infocomm Research, Connexis, Singapore]"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Connexis, Singapore","institution_ids":["https://openalex.org/I3005327000"]},{"raw_affiliation_string":"[Institute for Infocomm Research, Connexis, Singapore]","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014529529","display_name":"Jie Zheng","orcid":"https://orcid.org/0000-0001-6774-9786"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jie Zheng","raw_affiliation_strings":["Nanyang Technological University, Singapore","Nanyang Technological University (Singapore)"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Nanyang Technological University (Singapore)","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5046093778"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":2.0767,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.87229869,"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":"8","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":1.0,"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":1.0,"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/T10911","display_name":"Chemical Synthesis and Analysis","score":0.9926000237464905,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.9886000156402588,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.7729767560958862},{"id":"https://openalex.org/keywords/drug-target","display_name":"Drug target","score":0.7205367088317871},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7168098092079163},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5391109585762024},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5192251801490784},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.5164473056793213},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3202376663684845},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.17226237058639526},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.09706494212150574},{"id":"https://openalex.org/keywords/pharmacology","display_name":"Pharmacology","score":0.07355484366416931}],"concepts":[{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.7729767560958862},{"id":"https://openalex.org/C2989108626","wikidata":"https://www.wikidata.org/wiki/Q904407","display_name":"Drug target","level":2,"score":0.7205367088317871},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7168098092079163},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5391109585762024},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5192251801490784},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.5164473056793213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3202376663684845},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.17226237058639526},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.09706494212150574},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.07355484366416931},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2350176.2350178","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2350176.2350178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th International Workshop on Data Mining in Bioinformatics","raw_type":"proceedings-article"},{"id":"pmh:oai:dr.ntu.edu.sg:10356/98786","is_oa":false,"landing_page_url":"https://hdl.handle.net/10356/98786","pdf_url":null,"source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3383292388","display_name":null,"funder_award_id":"MOE2008-T2-1-074","funder_id":"https://openalex.org/F4320320751","funder_display_name":"Ministry of Education - Singapore"}],"funders":[{"id":"https://openalex.org/F4320320751","display_name":"Ministry of Education - Singapore","ror":"https://ror.org/01kcva023"},{"id":"https://openalex.org/F4320321026","display_name":"Ministry of Earth Sciences","ror":"https://ror.org/013cf5k59"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"},{"id":"https://openalex.org/F4320332495","display_name":"U.S. Bureau of Land Management","ror":"https://ror.org/03v0pmy70"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W30716843","https://openalex.org/W587662218","https://openalex.org/W1971106435","https://openalex.org/W1976526581","https://openalex.org/W1979537657","https://openalex.org/W2006286139","https://openalex.org/W2009313526","https://openalex.org/W2066201825","https://openalex.org/W2079745490","https://openalex.org/W2100672820","https://openalex.org/W2106029302","https://openalex.org/W2125200271","https://openalex.org/W2137507165","https://openalex.org/W2138512826","https://openalex.org/W2139736926","https://openalex.org/W2144000913","https://openalex.org/W2153838454","https://openalex.org/W2154896031","https://openalex.org/W2156077095","https://openalex.org/W2165674132"],"related_works":["https://openalex.org/W2371352078","https://openalex.org/W2953461625","https://openalex.org/W2077383796","https://openalex.org/W2080136900","https://openalex.org/W2999799752","https://openalex.org/W2372768926","https://openalex.org/W2054458431","https://openalex.org/W2115167491","https://openalex.org/W4390443207","https://openalex.org/W4391636780"],"abstract_inverted_index":{"In":[0,72],"pharmacology,":[1],"it":[2,116],"is":[3,58],"essential":[4],"to":[5,13,28,60,185],"identify":[6],"the":[7,77,94,118,158,172,182,198],"interactions":[8,35,141,162],"between":[9,142,163],"drug":[10,62,88,144],"and":[11,89,99,114,146,165,202],"targets":[12,100],"understand":[14],"its":[15,203],"effects.":[16],"Supervised":[17],"learning":[18],"with":[19,130],"Bipartite":[20],"Local":[21],"Model":[22],"(BLM)":[23],"recently":[24],"has":[25,135,154],"been":[26,155],"shown":[27],"be":[29,186],"effective":[30],"for":[31,85,139,181],"prediction":[32,140,206],"of":[33,41,121,152,160,169,192,200,207],"drug-target":[34,208],"by":[36,81],"first":[37],"predicting":[38,47,161],"target":[39,64,90,148],"proteins":[40],"a":[42,50,83,107],"given":[43],"known":[44,51,70],"drug,":[45],"then":[46,134],"drugs":[48,98,164],"targeting":[49],"protein.":[52],"However,":[53],"this":[54,73],"pure":[55],"\"local\"":[56],"model":[57,129],"inapplicable":[59],"new":[61,87,143,147],"or":[63],"candidates":[65,145],"that":[66,96],"currently":[67],"have":[68,101],"no":[69],"interactions.":[71,209],"paper,":[74],"we":[75,105],"extend":[76],"existing":[78],"BLM":[79,125],"method":[80],"integrating":[82],"strategy":[84,184],"handling":[86],"candidates.":[91,149],"Based":[92],"on":[93],"assumption":[95],"similar":[97,102],"interaction":[103],"profiles,":[104],"present":[106],"simple":[108],"neighbor-based":[109,131],"training":[110],"data":[111],"inferring":[112,132],"procedure":[113],"integrate":[115],"into":[117],"frame":[119],"work":[120],"BLM.":[122],"This":[123,196],"globalized":[124],"called":[126],"bipartite":[127],"local":[128],"(BLMN)":[133],"an":[136],"extended":[137],"functionality":[138],"Good":[150],"performance":[151],"BLMN":[153,201],"observed":[156],"in":[157,190,205],"experiment":[159],"four":[166],"important":[167],"categories":[168],"targets.":[170],"For":[171],"Nuclear":[173],"Receptors":[174],"dataset,":[175],"where":[176],"there":[177],"are":[178],"more":[179],"chances":[180],"presented":[183],"applied,":[187],"20%":[188],"improvement":[189],"terms":[191],"AUPR":[193],"was":[194],"achieved.":[195],"demonstrates":[197],"effectiveness":[199],"potential":[204]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2014,"cited_by_count":6}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}