{"id":"https://openalex.org/W4206176293","doi":"https://doi.org/10.1093/bib/bbab520","title":"Drug\u2013target interactions prediction via deep collaborative filtering with multiembeddings","display_name":"Drug\u2013target interactions prediction via deep collaborative filtering with multiembeddings","publication_year":2022,"publication_date":"2022-01-06","ids":{"openalex":"https://openalex.org/W4206176293","doi":"https://doi.org/10.1093/bib/bbab520","pmid":"https://pubmed.ncbi.nlm.nih.gov/35043158"},"language":"en","primary_location":{"id":"doi:10.1093/bib/bbab520","is_oa":false,"landing_page_url":"https://doi.org/10.1093/bib/bbab520","pdf_url":null,"source":{"id":"https://openalex.org/S91767247","display_name":"Briefings in Bioinformatics","issn_l":"1467-5463","issn":["1467-5463","1477-4054"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"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/A5016093001","display_name":"Ruolan Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruolan Chen","raw_affiliation_strings":["Department of Computer Science and Technology, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102998273","display_name":"Feng Xia","orcid":"https://orcid.org/0000-0002-4172-254X"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Xia","raw_affiliation_strings":["Department of Computer Science and Technology, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-4172-254X","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045468770","display_name":"Bing Hu","orcid":"https://orcid.org/0000-0002-9898-8656"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Hu","raw_affiliation_strings":["Department of Computer Science and Technology, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015486123","display_name":"Shuting Jin","orcid":"https://orcid.org/0000-0002-8113-9367"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]},{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuting Jin","raw_affiliation_strings":["Department of Computer Science and Technology, Xiamen University, Xiamen, China","National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I75867142"]},{"raw_affiliation_string":"National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026771763","display_name":"Xiangrong Liu","orcid":"https://orcid.org/0000-0001-9885-1978"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangrong Liu","raw_affiliation_strings":["Department of Computer Science and Technology, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0001-9885-1978","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I75867142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5026771763"],"corresponding_institution_ids":["https://openalex.org/I75867142"],"apc_list":{"value":4011,"currency":"USD","value_usd":4011},"apc_paid":null,"fwci":1.2932,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.81332137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"23","issue":"2","first_page":null,"last_page":null},"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9916999936103821,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6004732251167297},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.5505304336547852},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4330008625984192},{"id":"https://openalex.org/keywords/pharmacology","display_name":"Pharmacology","score":0.19390511512756348},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.17167139053344727}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6004732251167297},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.5505304336547852},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4330008625984192},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.19390511512756348},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.17167139053344727}],"mesh":[{"descriptor_ui":"D000076722","descriptor_name":"Drug Development","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D000076722","descriptor_name":"Drug Development","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D000076722","descriptor_name":"Drug Development","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1093/bib/bbab520","is_oa":false,"landing_page_url":"https://doi.org/10.1093/bib/bbab520","pdf_url":null,"source":{"id":"https://openalex.org/S91767247","display_name":"Briefings in Bioinformatics","issn_l":"1467-5463","issn":["1467-5463","1477-4054"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in Bioinformatics","raw_type":"journal-article"},{"id":"pmid:35043158","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35043158","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in bioinformatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G2841600477","display_name":null,"funder_award_id":"2017YFE0130600","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5091875258","display_name":"\u57fa\u4e8e\u8d28\u7c92\u548c\u83cc\u843d\u6f14\u5316\u7684\u4f53\u5185\u8ba1\u7b97\u7814\u7a76","funder_award_id":"61872309","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6312213820","display_name":"\u764c\u75c7miRNA\u8868\u8fbe\u8c31\u4e2d\u7684\u591aisomiR\u76f8\u4e92\u4f5c\u7528\u8bc6\u522b\u65b9\u6cd5\u7814\u7a76","funder_award_id":"62072385","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8390325463","display_name":"\u57fa\u4e8e\u566c\u83cc\u4f53\u548c\u8d28\u7c92\u7684\u795e\u7ecf\u819c\u7cfb\u7edf\u7814\u7a76","funder_award_id":"61772441","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8971624103","display_name":null,"funder_award_id":"62072384","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1976526581","https://openalex.org/W1988037271","https://openalex.org/W1991820123","https://openalex.org/W1998898494","https://openalex.org/W2066201825","https://openalex.org/W2087064593","https://openalex.org/W2106029302","https://openalex.org/W2114533766","https://openalex.org/W2117235735","https://openalex.org/W2119629574","https://openalex.org/W2122863289","https://openalex.org/W2124001239","https://openalex.org/W2128728535","https://openalex.org/W2137052779","https://openalex.org/W2142572836","https://openalex.org/W2161607603","https://openalex.org/W2499811908","https://openalex.org/W2514203454","https://openalex.org/W2556519015","https://openalex.org/W2558217333","https://openalex.org/W2604272474","https://openalex.org/W2605350416","https://openalex.org/W2613712744","https://openalex.org/W2615071493","https://openalex.org/W2753953057","https://openalex.org/W2945027804","https://openalex.org/W2952522777","https://openalex.org/W2966787092","https://openalex.org/W2972223935","https://openalex.org/W3021338900","https://openalex.org/W4301409532"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Drug-target":[0],"interactions":[1],"(DTIs)":[2],"prediction":[3,22,59,68,166],"research":[4],"presents":[5],"important":[6],"significance":[7],"for":[8,20],"promoting":[9],"the":[10,24,96,106,110,119,126,134,155],"development":[11],"of":[12,112],"modern":[13],"medicine":[14],"and":[15,32,36,103,115,130,160],"pharmacology.":[16],"Traditional":[17],"biochemical":[18],"experiments":[19,138],"DTIs":[21],"confront":[23],"challenges":[25],"including":[26],"long":[27],"time":[28],"period,":[29],"high":[30,33],"cost":[31],"failure":[34],"rate,":[35],"finally":[37],"leading":[38],"to":[39,89],"a":[40,55],"low-drug":[41],"productivity.":[42],"Chemogenomic-based":[43],"computational":[44],"methods":[45,142],"can":[46,73],"realize":[47],"high-throughput":[48],"prediction.":[49],"In":[50,117],"this":[51],"study,":[52],"we":[53],"develop":[54],"deep":[56],"collaborative":[57,66],"filtering":[58,67],"model":[60,69,120,156],"with":[61,70,139],"multiembeddings,":[62],"named":[63],"DCFME":[64,94,145],"(deep":[65],"multiembeddings),":[71],"which":[72],"jointly":[74],"utilize":[75],"multiple":[76],"feature":[77],"information":[78],"from":[79,109],"multiembeddings.":[80],"Two":[81],"different":[82],"representation":[83],"learning":[84],"algorithms":[85],"are":[86],"first":[87],"employed":[88],"extract":[90],"heterogeneous":[91],"network":[92],"features.":[93],"uses":[95],"generated":[97],"low-dimensional":[98],"dense":[99],"vectors":[100],"as":[101],"input,":[102],"then":[104],"simulates":[105],"drug-target":[107],"relationship":[108],"perspective":[111],"both":[113],"couplings":[114],"heterogeneity.":[116],"addition,":[118],"employs":[121],"focal":[122],"loss":[123,127],"that":[124,144],"concentrates":[125],"on":[128,151],"sparse":[129,152],"hard":[131],"samples":[132],"in":[133],"training":[135],"process.":[136],"Comparative":[137],"five":[140],"baseline":[141],"show":[143],"achieves":[146],"more":[147],"significant":[148],"performance":[149],"improvement":[150],"datasets.":[153],"Moreover,":[154],"has":[157],"better":[158],"robustness":[159],"generalization":[161],"capacity":[162],"under":[163],"several":[164],"harder":[165],"scenarios.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
