{"id":"https://openalex.org/W2049170138","doi":"https://doi.org/10.1145/2339530.2339700","title":"Transductive multi-label ensemble classification for protein function prediction","display_name":"Transductive multi-label ensemble classification for protein function prediction","publication_year":2012,"publication_date":"2012-08-12","ids":{"openalex":"https://openalex.org/W2049170138","doi":"https://doi.org/10.1145/2339530.2339700","mag":"2049170138"},"language":"en","primary_location":{"id":"doi:10.1145/2339530.2339700","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339700","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5111946342","display_name":"Guoxian Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoxian Yu","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090148962","display_name":"Carlotta Domeniconi","orcid":"https://orcid.org/0000-0003-2140-9596"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carlotta Domeniconi","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006581225","display_name":"Huzefa Rangwala","orcid":"https://orcid.org/0000-0003-0435-0035"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huzefa Rangwala","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053425174","display_name":"Guoji Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoji Zhang","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100701158","display_name":"Zhiwen Yu","orcid":"https://orcid.org/0000-0002-0935-5890"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwen Yu","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1077","last_page":"1085"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9991000294685364,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9991000294685364,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.9908000230789185,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/protein-function-prediction","display_name":"Protein function prediction","score":0.7146262526512146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6908727884292603},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.639656662940979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5615668296813965},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.5471240282058716},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5419533848762512},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5223827958106995},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5039963126182556},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.4842362105846405},{"id":"https://openalex.org/keywords/graph-kernel","display_name":"Graph kernel","score":0.46376773715019226},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4096522331237793},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39229726791381836},{"id":"https://openalex.org/keywords/protein-function","display_name":"Protein function","score":0.3182765245437622},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2648819088935852},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.19459819793701172},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.19285762310028076},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14690536260604858},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.124728262424469},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.07030856609344482}],"concepts":[{"id":"https://openalex.org/C207060522","wikidata":"https://www.wikidata.org/wiki/Q7251473","display_name":"Protein function prediction","level":4,"score":0.7146262526512146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6908727884292603},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.639656662940979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5615668296813965},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.5471240282058716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5419533848762512},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5223827958106995},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5039963126182556},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.4842362105846405},{"id":"https://openalex.org/C100595998","wikidata":"https://www.wikidata.org/wiki/Q11731931","display_name":"Graph kernel","level":5,"score":0.46376773715019226},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4096522331237793},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39229726791381836},{"id":"https://openalex.org/C2986374874","wikidata":"https://www.wikidata.org/wiki/Q8054","display_name":"Protein function","level":3,"score":0.3182765245437622},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2648819088935852},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.19459819793701172},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.19285762310028076},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14690536260604858},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.124728262424469},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.07030856609344482},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2339530.2339700","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339700","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":36,"referenced_works":["https://openalex.org/W55768394","https://openalex.org/W194789113","https://openalex.org/W1549435618","https://openalex.org/W1604842785","https://openalex.org/W1975414584","https://openalex.org/W1976463269","https://openalex.org/W2035055162","https://openalex.org/W2049633694","https://openalex.org/W2057508887","https://openalex.org/W2062649377","https://openalex.org/W2082380079","https://openalex.org/W2086453833","https://openalex.org/W2094124219","https://openalex.org/W2102037812","https://openalex.org/W2103017472","https://openalex.org/W2104290444","https://openalex.org/W2109743529","https://openalex.org/W2109886974","https://openalex.org/W2115629999","https://openalex.org/W2118712128","https://openalex.org/W2120688086","https://openalex.org/W2124649441","https://openalex.org/W2136490963","https://openalex.org/W2137917513","https://openalex.org/W2138312771","https://openalex.org/W2154455818","https://openalex.org/W2155440340","https://openalex.org/W2161444669","https://openalex.org/W2166468869","https://openalex.org/W2170960297","https://openalex.org/W2264065776","https://openalex.org/W2997701990","https://openalex.org/W3004387475","https://openalex.org/W6602286067","https://openalex.org/W6682494755","https://openalex.org/W6684705368"],"related_works":["https://openalex.org/W2769358851","https://openalex.org/W2189183545","https://openalex.org/W2110459882","https://openalex.org/W2964169082","https://openalex.org/W2828181497","https://openalex.org/W2153211825","https://openalex.org/W4300454542","https://openalex.org/W3212423325","https://openalex.org/W2604913466","https://openalex.org/W2204231418"],"abstract_inverted_index":{"Advances":[0],"in":[1,29],"biotechnology":[2],"have":[3,68],"made":[4],"available":[5],"multitudes":[6],"of":[7,23,98,110,162,171,181,185,196,204],"heterogeneous":[8,15],"proteomic":[9],"and":[10,71,107,127,157,187,190,198,227],"genomic":[11],"data.":[12],"Integrating":[13],"these":[14],"data":[16,36,105,133],"sources,":[17],"to":[18,49,67,200],"automatically":[19],"infer":[20],"the":[21,78,136,160,163,169,194,202],"function":[22,60,80,222],"proteins,":[24,101,182],"is":[25,55,121,168],"a":[26,39,51,59,84,114,124,130,149,172],"fundamental":[27],"challenge":[28],"computational":[30],"biology.":[31],"Several":[32],"approaches":[33,76],"represent":[34],"each":[35,154],"source":[37],"with":[38,103],"kernel":[40],"(similarity)":[41],"function.":[42],"The":[43,145],"resulting":[44],"kernels":[45],"are":[46,64],"then":[47,158],"integrated":[48],"determine":[50],"composite":[52,125,226],"kernel,":[53,126,156],"which":[54],"used":[56],"for":[57,132,153],"developing":[58],"prediction":[61,81,223],"model.":[62],"Proteins":[63],"also":[65,128],"found":[66],"multiple":[69,104,108,228],"roles":[70],"functions.":[72,191],"As":[73],"such,":[74],"several":[75,99],"cast":[77],"protein":[79,221],"problem":[82],"within":[83],"multi-label":[85,117,141,151],"learning":[86],"framework.":[87],"In":[88],"our":[89,214],"work":[90],"we":[91],"develop":[92,113],"an":[93],"approach":[94,147,215],"that":[95,120,176,213],"takes":[96],"advantage":[97],"unlabeled":[100],"along":[102],"sources":[106],"functions":[109,203],"proteins.":[111],"We":[112,192,211],"graph-based":[115,150],"transductive":[116,140],"classifier":[118,143,152],"(TMC)":[119],"evaluated":[122],"on":[123,225],"propose":[129],"method":[131],"integration":[134],"using":[135,207],"ensemble":[137,142],"framework,":[138],"called":[139],"(TMEC).":[144],"TMEC":[146,199],"trains":[148],"individual":[155,164],"combines":[159],"predictions":[161],"models.":[165],"Our":[166],"contribution":[167],"use":[170],"bi-relational":[173],"directed":[174],"graph":[175],"captures":[177],"relationships":[178],"between":[179,183,188],"pairs":[180,184],"functions,":[186],"proteins":[189,205],"evaluate":[193],"ability":[195],"TMC":[197],"predict":[201],"by":[206],"two":[208],"yeast":[209],"datasets.":[210],"show":[212],"performs":[216],"better":[217],"than":[218],"recently":[219],"proposed":[220],"methods":[224],"kernels.":[229]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":7},{"year":2013,"cited_by_count":6}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}