{"id":"https://openalex.org/W2745024368","doi":"https://doi.org/10.1145/3097983.3098103","title":"Convex Factorization Machine for Toxicogenomics Prediction","display_name":"Convex Factorization Machine for Toxicogenomics Prediction","publication_year":2017,"publication_date":"2017-08-04","ids":{"openalex":"https://openalex.org/W2745024368","doi":"https://doi.org/10.1145/3097983.3098103","mag":"2745024368"},"language":"en","primary_location":{"id":"doi:10.1145/3097983.3098103","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3097983.3098103","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3098103&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=3098103&type=pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046358579","display_name":"Makoto Yamada","orcid":"https://orcid.org/0000-0001-7508-5094"},"institutions":[{"id":"https://openalex.org/I4210126580","display_name":"RIKEN Center for Advanced Intelligence Project","ror":"https://ror.org/03ckxwf91","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210126580"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Makoto Yamada","raw_affiliation_strings":["RIKEN AIP, JST PRESTO, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"RIKEN AIP, JST PRESTO, Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017678179","display_name":"Wenzhao Lian","orcid":"https://orcid.org/0000-0002-0995-8229"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenzhao Lian","raw_affiliation_strings":["Vicarious, San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Vicarious, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004177716","display_name":"Amit Goyal","orcid":"https://orcid.org/0000-0002-4004-8039"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amit Goyal","raw_affiliation_strings":["Yahoo Research, Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Yahoo Research, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100765313","display_name":"Jianhui Chen","orcid":"https://orcid.org/0000-0003-0343-5176"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianhui Chen","raw_affiliation_strings":["Microsoft, Redmond , WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond , WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112259225","display_name":"Kishan Wimalawarne","orcid":null},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kishan Wimalawarne","raw_affiliation_strings":["Kyoto University, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028161029","display_name":"Suleiman A. Khan","orcid":"https://orcid.org/0000-0002-0823-4042"},"institutions":[{"id":"https://openalex.org/I133731052","display_name":"University of Helsinki","ror":"https://ror.org/040af2s02","country_code":"FI","type":"education","lineage":["https://openalex.org/I133731052"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Suleiman A. Khan","raw_affiliation_strings":["University of Helsinki, Helsinki, Finland"],"affiliations":[{"raw_affiliation_string":"University of Helsinki, Helsinki, Finland","institution_ids":["https://openalex.org/I133731052"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018305257","display_name":"Samuel Kaski","orcid":"https://orcid.org/0000-0003-1925-9154"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Samuel Kaski","raw_affiliation_strings":["Aalto University, Espoo, Finland"],"affiliations":[{"raw_affiliation_string":"Aalto University, Espoo, Finland","institution_ids":["https://openalex.org/I9927081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059001924","display_name":"Hiroshi Mamitsuka","orcid":"https://orcid.org/0000-0002-6607-5617"},"institutions":[{"id":"https://openalex.org/I4210088032","display_name":"Kyoto Bunkyo University","ror":"https://ror.org/0037an472","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210088032"]},{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Mamitsuka","raw_affiliation_strings":["Kyoto University &amp; Aalto University, Uji, Japan"],"affiliations":[{"raw_affiliation_string":"Kyoto University &amp; Aalto University, Uji, Japan","institution_ids":["https://openalex.org/I4210088032","https://openalex.org/I22299242"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029392006","display_name":"Yi Chang","orcid":"https://orcid.org/0000-0003-2697-8093"},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Chang","raw_affiliation_strings":["Huawei Research America, Santa Clara, CA, USA"],"affiliations":[{"raw_affiliation_string":"Huawei Research America, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I4210146936"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5046358579"],"corresponding_institution_ids":["https://openalex.org/I4210126580"],"apc_list":null,"apc_paid":null,"fwci":1.8983,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.85319516,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1215","last_page":"1224"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9539999961853027,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10885","display_name":"Gene expression and cancer classification","score":0.9347000122070312,"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/matrix-decomposition","display_name":"Matrix decomposition","score":0.616460382938385},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5702813267707825},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.5534298419952393},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.47078341245651245},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.4568397104740143},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.44680914282798767},{"id":"https://openalex.org/keywords/quadratic-programming","display_name":"Quadratic programming","score":0.4115692675113678},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34856241941452026},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.3352311849594116},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3011326193809509},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.06874564290046692}],"concepts":[{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.616460382938385},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5702813267707825},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.5534298419952393},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.47078341245651245},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4568397104740143},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.44680914282798767},{"id":"https://openalex.org/C81845259","wikidata":"https://www.wikidata.org/wiki/Q290117","display_name":"Quadratic programming","level":2,"score":0.4115692675113678},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34856241941452026},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.3352311849594116},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3011326193809509},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.06874564290046692},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3097983.3098103","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3097983.3098103","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3098103&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3097983.3098103","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3097983.3098103","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3098103&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320912","display_name":"Ministry of Education, Culture, Sports, Science and Technology","ror":"https://ror.org/048rj2z13"},{"id":"https://openalex.org/F4320321108","display_name":"Academy of Finland","ror":"https://ror.org/05k73zm37"},{"id":"https://openalex.org/F4320321855","display_name":"Tekes","ror":"https://ror.org/02ag8cq23"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320338111","display_name":"Precursory Research for Embryonic Science and Technology","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2745024368.pdf","grobid_xml":"https://content.openalex.org/works/W2745024368.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W905619","https://openalex.org/W884417352","https://openalex.org/W1502375784","https://openalex.org/W1532737361","https://openalex.org/W1539811621","https://openalex.org/W1574851760","https://openalex.org/W1575641478","https://openalex.org/W1775587472","https://openalex.org/W1824659232","https://openalex.org/W1951829412","https://openalex.org/W1963826206","https://openalex.org/W1966096622","https://openalex.org/W1998635907","https://openalex.org/W2000215628","https://openalex.org/W2006262045","https://openalex.org/W2007437396","https://openalex.org/W2013029404","https://openalex.org/W2029119963","https://openalex.org/W2054141820","https://openalex.org/W2065049914","https://openalex.org/W2087312216","https://openalex.org/W2094286023","https://openalex.org/W2100672820","https://openalex.org/W2102515753","https://openalex.org/W2103325283","https://openalex.org/W2103972604","https://openalex.org/W2105767123","https://openalex.org/W2112487361","https://openalex.org/W2117420919","https://openalex.org/W2118080949","https://openalex.org/W2121604817","https://openalex.org/W2127249498","https://openalex.org/W2130336429","https://openalex.org/W2136885855","https://openalex.org/W2139750075","https://openalex.org/W2145831204","https://openalex.org/W2146130798","https://openalex.org/W2159023782","https://openalex.org/W2159940675","https://openalex.org/W2160047866","https://openalex.org/W2170148990","https://openalex.org/W2247380138","https://openalex.org/W2271689356","https://openalex.org/W2295739661","https://openalex.org/W2337878656","https://openalex.org/W2339666411","https://openalex.org/W2402753422","https://openalex.org/W2482092413","https://openalex.org/W2590518419","https://openalex.org/W2611328865","https://openalex.org/W2623253964","https://openalex.org/W2952647294","https://openalex.org/W2964163305","https://openalex.org/W3003365835","https://openalex.org/W3103867094","https://openalex.org/W4231990774"],"related_works":["https://openalex.org/W1973739845","https://openalex.org/W119752240","https://openalex.org/W2322281151","https://openalex.org/W2011094784","https://openalex.org/W2756132392","https://openalex.org/W3035814349","https://openalex.org/W4285101096","https://openalex.org/W4382725876","https://openalex.org/W2084892497","https://openalex.org/W2115614142"],"abstract_inverted_index":{"We":[0,160],"introduce":[1],"the":[2,13,27,31,34,38,45,89,98,152,171],"convex":[3,10],"factorization":[4,115,124,129,184],"machine":[5],"(CFM),":[6],"which":[7],"is":[8,70,94,101,112],"a":[9,22,49,75,83,113,164,175,181],"variant":[11],"of":[12,65,92,174,177],"widely":[14],"used":[15,121,145],"Factorization":[16],"Machines":[17],"(FMs).":[18],"Specifically,":[19],"we":[20,43,148],"employ":[21],"linear+quadratic":[23],"model":[24],"and":[25,33,53,105,117,130,139,143],"regularize":[26],"linear":[28],"term":[29,36],"with":[30,37,59],"\u21132-regularizer":[32],"quadratic":[35],"trace":[39],"norm":[40],"regularizer.":[41],"Then,":[42],"formulate":[44],"CFM":[46,66,111,154,169],"optimization":[47,57],"as":[48],"semidefinite":[50],"programming":[51],"problem":[52],"propose":[54],"an":[55],"efficient":[56],"procedure":[58],"Hazan's":[60],"algorithm.":[61],"A":[62],"key":[63],"advantage":[64],"over":[67],"existing":[68],"FMs":[69,80,93],"that":[71,151,168],"it":[72],"can":[73,106,118],"find":[74],"globally":[76],"optimal":[77,86],"solution,":[78],"while":[79],"may":[81],"get":[82],"poor":[84],"locally":[85],"solution":[87],"since":[88],"objective":[90],"function":[91],"non-convex.":[95],"In":[96],"addition,":[97],"proposed":[99,153],"algorithm":[100],"simple":[102],"yet":[103],"effective":[104],"be":[107,120],"implemented":[108],"easily.":[109],"Finally,":[110],"general":[114],"method":[116],"also":[119],"for":[122],"other":[123],"problems,":[125,133],"including":[126,137],"multi-view":[127],"matrix":[128],"tensor":[131,183],"completion":[132],"in":[134,163],"various":[135],"domains":[136],"toxicogenomics":[138,165],"bioinformatics.":[140],"Through":[141],"synthetic":[142],"traditionally":[144],"movielens":[146],"datasets,":[147],"first":[149],"show":[150,162],"achieves":[155],"results":[156],"competitive":[157],"to":[158],"FMs.":[159],"then":[161],"prediction":[166],"task":[167],"predicts":[170],"toxic":[172],"outcomes":[173],"collection":[176],"drugs":[178],"better":[179],"than":[180],"state-of-the-art":[182],"method.":[185]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
