{"id":"https://openalex.org/W2972370735","doi":"https://doi.org/10.1145/3307339.3342136","title":"Deep Learning for High-Order Drug-Drug Interaction Prediction","display_name":"Deep Learning for High-Order Drug-Drug Interaction Prediction","publication_year":2019,"publication_date":"2019-09-04","ids":{"openalex":"https://openalex.org/W2972370735","doi":"https://doi.org/10.1145/3307339.3342136","mag":"2972370735"},"language":"en","primary_location":{"id":"doi:10.1145/3307339.3342136","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3307339.3342136","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3307339.3342136","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3307339.3342136","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101652188","display_name":"Bo Peng","orcid":"https://orcid.org/0000-0002-1183-9165"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bo Peng","raw_affiliation_strings":["Ohio State University, Columbus, OH, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ohio State University, Columbus, OH, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028997621","display_name":"Xia Ning","orcid":"https://orcid.org/0000-0002-6842-1165"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xia Ning","raw_affiliation_strings":["Ohio State University, Columbus, OH, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ohio State University, Columbus, OH, USA","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":null,"apc_paid":null,"fwci":1.8775,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.85635162,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"197","last_page":"206"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9993000030517578,"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":0.9993000030517578,"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/T11943","display_name":"Pharmacovigilance and Adverse Drug Reactions","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3005","display_name":"Toxicology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.977400004863739,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.6533738374710083},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6349822282791138},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6304558515548706},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.6273958086967468},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6072615385055542},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5641151070594788},{"id":"https://openalex.org/keywords/drug-drug-interaction","display_name":"Drug-drug interaction","score":0.48243793845176697},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.47123584151268005},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.21942374110221863},{"id":"https://openalex.org/keywords/pharmacology","display_name":"Pharmacology","score":0.19961071014404297}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6533738374710083},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6349822282791138},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6304558515548706},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.6273958086967468},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6072615385055542},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5641151070594788},{"id":"https://openalex.org/C2910466267","wikidata":"https://www.wikidata.org/wiki/Q718753","display_name":"Drug-drug interaction","level":3,"score":0.48243793845176697},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.47123584151268005},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.21942374110221863},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.19961071014404297},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3307339.3342136","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3307339.3342136","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3307339.3342136","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3307339.3342136","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3307339.3342136","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3307339.3342136","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1058203011","display_name":null,"funder_award_id":"IIS-1827472","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G51725407","display_name":null,"funder_award_id":"IIS-1855501","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5826519190","display_name":"SCH: INT: Mining Drug-Drug Interaction Induced Adverse Effects from Health Record Databases","funder_award_id":"1827472","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G838880354","display_name":"CRII: III: Computational Methods to Explore Big Bioassay Data for Better Compound Prioritization","funder_award_id":"1855501","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2972370735.pdf","grobid_xml":"https://content.openalex.org/works/W2972370735.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W92486268","https://openalex.org/W104574262","https://openalex.org/W113750267","https://openalex.org/W1522301498","https://openalex.org/W1686939912","https://openalex.org/W2007921205","https://openalex.org/W2024985940","https://openalex.org/W2035497846","https://openalex.org/W2048380327","https://openalex.org/W2053076602","https://openalex.org/W2056876304","https://openalex.org/W2061061337","https://openalex.org/W2065751466","https://openalex.org/W2086169342","https://openalex.org/W2091439417","https://openalex.org/W2096451472","https://openalex.org/W2106618845","https://openalex.org/W2109482131","https://openalex.org/W2123089829","https://openalex.org/W2135037015","https://openalex.org/W2137775956","https://openalex.org/W2148488766","https://openalex.org/W2152267531","https://openalex.org/W2153579005","https://openalex.org/W2187089797","https://openalex.org/W2232325453","https://openalex.org/W2282002114","https://openalex.org/W2330066387","https://openalex.org/W2412417578","https://openalex.org/W2518086895","https://openalex.org/W2570516417","https://openalex.org/W2584239330","https://openalex.org/W2589489260","https://openalex.org/W2604738573","https://openalex.org/W2765492483","https://openalex.org/W2770257788","https://openalex.org/W2785934082","https://openalex.org/W2786016794","https://openalex.org/W2897753544","https://openalex.org/W3022637862","https://openalex.org/W6603835122"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W2395332729","https://openalex.org/W3082755771","https://openalex.org/W2407625558","https://openalex.org/W2485789143","https://openalex.org/W2040338448","https://openalex.org/W2415873281","https://openalex.org/W2463344692","https://openalex.org/W2710539784","https://openalex.org/W4248267070"],"abstract_inverted_index":{"Drug-drug":[0],"interactions":[1],"(DDIs)":[2],"and":[3,29,73,85,94,110,122,170],"their":[4,123,146,166],"associated":[5],"adverse":[6],"drug":[7,134,149,158,175,181],"reactions":[8],"(ADRs)":[9],"represent":[10],"a":[11,66],"significant":[12],"detriment":[13],"to":[14,35,54,131,138],"the":[15,105,116,184,191],"public":[16],"health.":[17],"Existing":[18],"research":[19],"on":[20,25,89,98,142,165,174],"DDIs":[21,121],"is":[22,32,129,190],"primarily":[23],"focused":[24],"pairwise":[26],"DDI":[27,42,76,92,100,197],"detection":[28],"prediction.":[30,43,77,101,198],"It":[31],"highly":[33],"needed":[34],"develop":[36],"effective":[37],"computational":[38],"tools":[39],"for":[40,71,157,195],"high-order":[41,59,75,91,120,196],"Here":[44],"we":[45,64],"show":[46],"that":[47],"deep":[48,67,111,193],"learning":[49,68,112,178],"can":[50,152],"be":[51],"effectively":[52],"utilized":[53],"predict":[55,155],"ADRs":[56,156],"induced":[57,124],"from":[58,145,179],"DDIs.":[60],"In":[61,126],"this":[62],"manuscript,":[63],"present":[65],"model":[69,194],"D3I":[70,79,109,128,151,189],"cardinality-invariant":[72],"order-invariant":[74],"The":[78],"models":[80,97,113],"achieve":[81],"0.740":[82],"F1":[83],"value":[84,88],"0.847":[86],"AUC":[87],"balanced":[90],"prediction,":[93],"outperform":[95],"other":[96],"order-2":[99],"These":[102],"results":[103],"demonstrate":[104],"strong":[106],"potential":[107],"of":[108,119,186],"in":[114,148,160],"tackling":[115],"prediction":[117,173],"problems":[118],"ADRs.":[125],"addition,":[127],"able":[130],"derive":[132],"single":[133,143,163],"representations,":[135],"which":[136,161],"conform":[137],"our":[139,187],"current":[140],"knowledge":[141],"drugs,":[144],"behaviors":[147],"combinations.":[150,182],"also":[153],"correctly":[154],"combinations":[159],"no":[162],"drugs":[164],"own":[167],"induce":[168],"ADRs,":[169],"improve":[171],"ADR":[172],"pairs":[176],"by":[177],"all":[180],"To":[183],"best":[185],"knowledge,":[188],"first":[192]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}