{"id":"https://openalex.org/W2014218775","doi":"https://doi.org/10.1109/bibm.2012.6392630","title":"Drug-induced QT prolongation prediction using co-regularized multi-view learning","display_name":"Drug-induced QT prolongation prediction using co-regularized multi-view learning","publication_year":2012,"publication_date":"2012-10-01","ids":{"openalex":"https://openalex.org/W2014218775","doi":"https://doi.org/10.1109/bibm.2012.6392630","mag":"2014218775"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2012.6392630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2012.6392630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Bioinformatics and Biomedicine","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/A5100412765","display_name":"Jintao Zhang","orcid":"https://orcid.org/0000-0001-6909-0612"},"institutions":[{"id":"https://openalex.org/I146416000","display_name":"University of Kansas","ror":"https://ror.org/001tmjg57","country_code":"US","type":"education","lineage":["https://openalex.org/I146416000"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jintao Zhang","raw_affiliation_strings":["Center for Bioinformatics, University of Kansas, Lawrence, KS, USA","Center for Bioinformatics, University of Kansas, Lawrence, KS 66047"],"affiliations":[{"raw_affiliation_string":"Center for Bioinformatics, University of Kansas, Lawrence, KS, USA","institution_ids":["https://openalex.org/I146416000"]},{"raw_affiliation_string":"Center for Bioinformatics, University of Kansas, Lawrence, KS 66047","institution_ids":["https://openalex.org/I146416000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105352906","display_name":"Jun Huan","orcid":"https://orcid.org/0000-0003-4929-2617"},"institutions":[{"id":"https://openalex.org/I146416000","display_name":"University of Kansas","ror":"https://ror.org/001tmjg57","country_code":"US","type":"education","lineage":["https://openalex.org/I146416000"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Huan","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA","Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA","institution_ids":["https://openalex.org/I146416000"]},{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045","institution_ids":["https://openalex.org/I146416000"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100412765"],"corresponding_institution_ids":["https://openalex.org/I146416000"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09234104,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9986000061035156,"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":0.9986000061035156,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9761999845504761,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9707000255584717,"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/regularization","display_name":"Regularization (linguistics)","score":0.6138461828231812},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.5270640850067139},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47156402468681335},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4506925046443939},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.4137302041053772},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4005824327468872},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3508196473121643},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3388326168060303},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.22756388783454895},{"id":"https://openalex.org/keywords/pharmacology","display_name":"Pharmacology","score":0.17203378677368164}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6138461828231812},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.5270640850067139},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47156402468681335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4506925046443939},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.4137302041053772},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4005824327468872},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3508196473121643},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3388326168060303},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.22756388783454895},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.17203378677368164},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2012.6392630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2012.6392630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Bioinformatics and Biomedicine","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320332163","display_name":"U.S. Food and Drug Administration","ror":"https://ror.org/034xvzb47"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W53987483","https://openalex.org/W1516550929","https://openalex.org/W1554944419","https://openalex.org/W1970333224","https://openalex.org/W1998444222","https://openalex.org/W2009313526","https://openalex.org/W2040319666","https://openalex.org/W2046135503","https://openalex.org/W2051669046","https://openalex.org/W2072427616","https://openalex.org/W2075118281","https://openalex.org/W2083299971","https://openalex.org/W2093024696","https://openalex.org/W2101202052","https://openalex.org/W2103024562","https://openalex.org/W2113927644","https://openalex.org/W2127689004","https://openalex.org/W2137618884","https://openalex.org/W2139212933","https://openalex.org/W2145137957","https://openalex.org/W2153635508","https://openalex.org/W2155342973","https://openalex.org/W2164078599","https://openalex.org/W2169797000","https://openalex.org/W2608505863","https://openalex.org/W3020570927","https://openalex.org/W3104527978","https://openalex.org/W3120421331","https://openalex.org/W4236796448","https://openalex.org/W6602211964","https://openalex.org/W6684045843"],"related_works":["https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2053286651","https://openalex.org/W2181743346","https://openalex.org/W2187401768","https://openalex.org/W2181413294","https://openalex.org/W2989452537","https://openalex.org/W2052122378","https://openalex.org/W2544423928","https://openalex.org/W2062023542"],"abstract_inverted_index":{"Drug-induced":[0],"QT":[1,16,32,105],"prolongation":[2,17,33,106],"is":[3,11],"a":[4,84],"major":[5],"life-threatening":[6],"adverse":[7],"drug":[8,24],"effect.":[9],"It":[10],"crucial":[12],"to":[13,45,82,146],"predict":[14],"the":[15,110,124,136,142],"effect":[18,107],"as":[19,21],"early":[20],"possible":[22],"in":[23,88,123,135],"development,":[25],"however,":[26],"data":[27,51,57],"on":[28],"drugs":[29],"that":[30,74,165],"induce":[31],"are":[34,61,68,144],"very":[35],"limited":[36,65],"and":[37,50,63,108,140,157,160],"noisy.":[38],"Multi-view":[39],"learning":[40,49,162],"(MVL)":[41],"has":[42],"been":[43],"applied":[44],"many":[46],"challenging":[47],"machine":[48],"mining":[52],"problems,":[53],"especially":[54],"when":[55],"complex":[56],"from":[58],"diverse":[59],"domains":[60],"involved":[62],"only":[64],"labeled":[66],"examples":[67],"available.":[69],"Unlike":[70],"existing":[71],"MVL":[72,100,159],"methods":[73,163],"use":[75],"l":[76,94,111,127],"<sub":[77,95,112,128],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[78,96,113,129],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sub>":[79],"-norm":[80,98,115,131],"co-regularization":[81,132],"obtain":[83],"smooth":[85],"objective":[86,117],"function,":[87],"this":[89],"paper":[90],"we":[91],"proposed":[92,155],"an":[93],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[97,114,130],"co-regularized":[99,116],"algorithm":[101],"for":[102,119],"predicting":[103],"drug-induced":[104],"reformulate":[109],"function":[118],"deriving":[120],"its":[121],"gradient":[122],"analytic":[125],"form.":[126],"enforces":[133],"sparsity":[134],"learned":[137],"mapping":[138],"functions":[139],"hence":[141],"results":[143],"expected":[145],"be":[147],"more":[148],"interpr\u00e9table.":[149],"Comprehensive":[150],"experimental":[151],"comparisons":[152],"between":[153],"our":[154,166],"method":[156,167],"previous":[158],"single-view":[161],"demonstrate":[164],"significantly":[168],"outperforms":[169],"those":[170],"baseline":[171],"methods.":[172]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}