{"id":"https://openalex.org/W4387848857","doi":"https://doi.org/10.1145/3583780.3615033","title":"REST: Drug-Drug Interaction Prediction via Reinforced Student-Teacher Curriculum Learning","display_name":"REST: Drug-Drug Interaction Prediction via Reinforced Student-Teacher Curriculum Learning","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387848857","doi":"https://doi.org/10.1145/3583780.3615033"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615033","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615033","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615033","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615033","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078263563","display_name":"Xinhang Li","orcid":"https://orcid.org/0000-0001-8294-0589"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinhang Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062726606","display_name":"Zhaopeng Qiu","orcid":"https://orcid.org/0000-0001-8622-2402"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhaopeng Qiu","raw_affiliation_strings":["BOSS Zhipin, Beijing, China"],"affiliations":[{"raw_affiliation_string":"BOSS Zhipin, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100645854","display_name":"Xiangyu Zhao","orcid":"https://orcid.org/0000-0003-2926-4416"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiangyu Zhao","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100419741","display_name":"Yong Zhang","orcid":"https://orcid.org/0000-0001-8803-2055"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058043497","display_name":"Chunxiao Xing","orcid":"https://orcid.org/0000-0001-9390-3097"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunxiao Xing","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100352418","display_name":"Xian Wu","orcid":"https://orcid.org/0000-0003-1118-9710"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xian Wu","raw_affiliation_strings":["Tencent Jarvis Lab, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent Jarvis Lab, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5078263563"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.3924,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6623187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1278","last_page":"1287"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9947999715805054,"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":0.9947999715805054,"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/T10028","display_name":"Topic Modeling","score":0.9918000102043152,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9868000149726868,"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/computer-science","display_name":"Computer science","score":0.6769838929176331},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6373698711395264},{"id":"https://openalex.org/keywords/rest","display_name":"Rest (music)","score":0.574462354183197},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5703278183937073},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5455822348594666},{"id":"https://openalex.org/keywords/curriculum","display_name":"Curriculum","score":0.5335968136787415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.526477575302124},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4647158086299896},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44892048835754395},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.423621267080307},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4172077178955078},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.09679317474365234},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09622323513031006},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08845764398574829}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6769838929176331},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6373698711395264},{"id":"https://openalex.org/C77265313","wikidata":"https://www.wikidata.org/wiki/Q879844","display_name":"Rest (music)","level":2,"score":0.574462354183197},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5703278183937073},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5455822348594666},{"id":"https://openalex.org/C47177190","wikidata":"https://www.wikidata.org/wiki/Q207137","display_name":"Curriculum","level":2,"score":0.5335968136787415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.526477575302124},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4647158086299896},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44892048835754395},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.423621267080307},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4172077178955078},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.09679317474365234},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09622323513031006},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08845764398574829},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3583780.3615033","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615033","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615033","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/113f083a-5630-486f-9b23-a76764403c80","is_oa":true,"landing_page_url":"https://hdl.handle.net/2031/113f083a-5630-486f-9b23-a76764403c80","pdf_url":null,"source":{"id":"https://openalex.org/S7407055387","display_name":"CityU Scholars","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Li, X, Qiu, Z, Zhao, X, Zhang, Y, Xing, C & Wu, X 2023, REST : Drug-Drug Interaction Prediction via Reinforced Student-Teacher Curriculum Learning. in CIKM \u201923 : Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, pp. 1278-1287, 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023), Birmingham, United Kingdom, 21/10/23. https://doi.org/10.1145/3583780.3615033","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"doi:10.1145/3583780.3615033","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615033","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615033","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6700000166893005,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2374448338","display_name":null,"funder_award_id":"62202332","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2529879782","display_name":null,"funder_award_id":"92067206","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2720237306","display_name":null,"funder_award_id":"62202332, 92067206","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G553902691","display_name":null,"funder_award_id":"Start-up","funder_id":"https://openalex.org/F4320309893","funder_display_name":"City University of Hong Kong"},{"id":"https://openalex.org/G5854786607","display_name":null,"funder_award_id":"Tianjin","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5939423041","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6058138561","display_name":null,"funder_award_id":", No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7174558747","display_name":null,"funder_award_id":"Group","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7271486925","display_name":null,"funder_award_id":"9360163","funder_id":"https://openalex.org/F4320309893","funder_display_name":"City University of Hong Kong"},{"id":"https://openalex.org/G8226490486","display_name":null,"funder_award_id":"9360163","funder_id":"https://openalex.org/F4320334123","funder_display_name":"Hong Kong Institute for Data Science"}],"funders":[{"id":"https://openalex.org/F4320309893","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23"},{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"},{"id":"https://openalex.org/F4320318398","display_name":"Ant Group","ror":null},{"id":"https://openalex.org/F4320319065","display_name":"Aromatic Plant Research Center","ror":"https://ror.org/05eebgw43"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334123","display_name":"Hong Kong Institute for Data Science","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387848857.pdf","grobid_xml":"https://content.openalex.org/works/W4387848857.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W1979845492","https://openalex.org/W1988037271","https://openalex.org/W2091439417","https://openalex.org/W2106417713","https://openalex.org/W2117397379","https://openalex.org/W2154851992","https://openalex.org/W2296073425","https://openalex.org/W2301396653","https://openalex.org/W2416955410","https://openalex.org/W2432356473","https://openalex.org/W2591704587","https://openalex.org/W2767891136","https://openalex.org/W2802200505","https://openalex.org/W2885593519","https://openalex.org/W2962756421","https://openalex.org/W2972758308","https://openalex.org/W3024894285","https://openalex.org/W3034768035","https://openalex.org/W3038563803","https://openalex.org/W3087156149","https://openalex.org/W3089843526","https://openalex.org/W3094497296","https://openalex.org/W3104097132","https://openalex.org/W3139253280","https://openalex.org/W3153293286","https://openalex.org/W3156650687","https://openalex.org/W4212804458","https://openalex.org/W4220894606","https://openalex.org/W4224227049","https://openalex.org/W4226204438","https://openalex.org/W4281850946","https://openalex.org/W4290874950","https://openalex.org/W4324323345","https://openalex.org/W4384774596","https://openalex.org/W4384828482","https://openalex.org/W4385567542","https://openalex.org/W6718112784"],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W2291782699","https://openalex.org/W1993948687","https://openalex.org/W2000169967","https://openalex.org/W2112883198"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1],"of":[2,48,62,90,100,128,179,191,207],"drug-drug":[3],"interaction":[4],"(DDI)":[5],"is":[6,73,97,183],"crucial":[7],"to":[8,29,55,74,79,84,120,157,162,168],"achieving":[9],"effective":[10,118],"decision-making":[11],"in":[12,177,211],"medical":[13,49,91],"treatment":[14],"for":[15,117,188,215],"both":[16],"doctors":[17],"and":[18,34,88,142],"patients.":[19],"Recently,":[20],"many":[21],"deep":[22],"learning":[23,114],"based":[24],"methods":[25],"have":[26,40,202],"been":[27],"proposed":[28,209],"learn":[30],"from":[31],"drug-related":[32],"features":[33],"conduct":[35],"DDI":[36,56,63,212,217],"prediction.":[37],"These":[38],"works":[39],"achieved":[41],"promising":[42],"results.":[43],"However,":[44],"the":[45,66,81,85,140,147,152,164,169,174,180,186,192,204,216],"extreme":[46],"imbalance":[47,123],"data":[50],"poses":[51],"a":[52,59,93,101,110,134,143],"serious":[53],"problem":[54],"prediction,":[57,213],"where":[58],"small":[60],"fraction":[61],"types":[64,218],"occupy":[65],"majority":[67],"training":[68],"data.":[69,82],"A":[70],"straightforward":[71],"way":[72],"develop":[75],"an":[76,159],"appropriate":[77,160],"policy":[78,96,189],"sample":[80,158],"Due":[83],"high":[86],"complexity":[87],"speciality":[89],"science,":[92],"dynamic":[94],"learnable":[95],"required":[98],"instead":[99],"heuristic,":[102],"uniform":[103],"or":[104],"static":[105],"one.":[106],"Therefore,":[107],"we":[108],"propose":[109],"REinforced":[111],"Student-Teacher":[112],"curriculum":[113],"model":[115,154,166,171,182,210],"(REST)":[116],"sampling":[119],"tackle":[121],"this":[122],"problem.":[124],"Specifically,":[125],"REST":[126],"consists":[127],"two":[129,199],"interactive":[130],"parts,":[131],"which":[132],"are":[133],"heterogeneous":[135],"graph":[136],"neural":[137],"network":[138],"as":[139,146,185],"student":[141,165,170,181],"reinforced":[144],"sampler":[145],"teacher.":[148],"In":[149],"each":[150],"interaction,":[151],"teacher":[153,193],"takes":[155],"action":[156],"batch":[161],"train":[163],"according":[167],"state":[172],"while":[173],"cumulated":[175],"improvement":[176],"performance":[178],"treated":[184],"reward":[187],"gradient":[190],"model.":[194],"The":[195],"experimental":[196],"results":[197],"on":[198],"benchmarking":[200],"datasets":[201],"demonstrated":[203],"significant":[205],"effectiveness":[206],"our":[208],"especially":[214],"with":[219],"low":[220],"frequency.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}