{"id":"https://openalex.org/W7126068805","doi":"https://doi.org/10.1109/bibm66473.2025.11356040","title":"Interpretable Herb-Disease Association Prediction with Molecular-Aware Multi-View Representation Learning","display_name":"Interpretable Herb-Disease Association Prediction with Molecular-Aware Multi-View Representation Learning","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126068805","doi":"https://doi.org/10.1109/bibm66473.2025.11356040"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5043892080","display_name":"Zeheng Zhong","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zeheng Zhong","raw_affiliation_strings":["School of Software and Microelectronics, Peking University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Software and Microelectronics, Peking University,Beijing,China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057740181","display_name":"H J Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongzhi Liu","raw_affiliation_strings":["School of Software and Microelectronics, Peking University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Software and Microelectronics, Peking University,Beijing,China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114194211","display_name":"zhonghai wu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhonghai Wu","raw_affiliation_strings":["Peking University,National Engineering Research Center for Software Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Peking University,National Engineering Research Center for Software Engineering,Beijing,China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043892080"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75570765,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4529","last_page":"4534"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.3711000084877014,"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":0.3711000084877014,"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/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.367900013923645,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.07190000265836716,"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/interpretability","display_name":"Interpretability","score":0.8482000231742859},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5662000179290771},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.541100025177002},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4587000012397766},{"id":"https://openalex.org/keywords/credibility","display_name":"Credibility","score":0.4447000026702881},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4212000072002411},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.3393999934196472},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.33739998936653137}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8482000231742859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6658999919891357},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6521000266075134},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6322000026702881},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5662000179290771},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.541100025177002},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4587000012397766},{"id":"https://openalex.org/C2780224610","wikidata":"https://www.wikidata.org/wiki/Q1530061","display_name":"Credibility","level":2,"score":0.4447000026702881},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4212000072002411},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.3393999934196472},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3384999930858612},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C2909810673","wikidata":"https://www.wikidata.org/wiki/Q261503","display_name":"Chinese herbs","level":4,"score":0.2913999855518341},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C2780876136","wikidata":"https://www.wikidata.org/wiki/Q900986","display_name":"Schisandra chinensis","level":4,"score":0.27250000834465027},{"id":"https://openalex.org/C103697762","wikidata":"https://www.wikidata.org/wiki/Q4112105","display_name":"Virtual screening","level":3,"score":0.2567000091075897},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7677721048","display_name":null,"funder_award_id":"2022YFB2703301","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2899291554","https://openalex.org/W2913843460","https://openalex.org/W4221074742","https://openalex.org/W4291826025","https://openalex.org/W4317569422","https://openalex.org/W4372323219","https://openalex.org/W4386170693","https://openalex.org/W4392393090","https://openalex.org/W4399391337","https://openalex.org/W4406852232"],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"herb-disease":[1,95],"association":[2,96],"plays":[3],"a":[4,94,126,158],"crucial":[5],"role":[6],"in":[7,67,79],"accelerating":[8],"herb":[9],"repositioning":[10],"and":[11,40,45,52,85,104,121,137,149,168,171],"expanding":[12],"clinical":[13],"applications":[14],"of":[15,22,43,70,77,146,193],"Traditional":[16],"Chinese":[17],"Medicine":[18],"(TCM).":[19],"However,":[20],"most":[21],"the":[23,27,33,68,75,89,117,182,191,194],"existing":[24,56,80],"methods":[25,57,81],"neglect":[26],"fine-grained":[28,143],"biomedical":[29],"information,":[30],"such":[31],"as":[32,108,110],"molecular":[34,144],"structures,":[35],"which":[36,101],"leads":[37],"to":[38,62,114,152,162,180],"inaccurate":[39],"incomplete":[41],"representations":[42],"herbs":[44,120,167],"diseases.":[46,122],"Since":[47],"TCM":[48,136],"involves":[49],"multiple":[50],"components":[51],"modulates":[53],"various":[54],"targets,":[55],"lack":[58,76],"an":[59],"effective":[60],"approach":[61],"represent":[63],"these":[64],"relations,":[65],"resulting":[66],"omission":[69],"critical":[71],"information.":[72],"In":[73],"addition,":[74],"interpretability":[78],"limits":[82],"their":[83],"credibility":[84],"acceptance.":[86],"To":[87],"tackle":[88],"above":[90],"problems,":[91],"we":[92,124,175],"propose":[93],"prediction":[97],"framework,":[98],"named":[99],"MVGP,":[100],"incorporates":[102],"Multi-View":[103],"multi-Granularity":[105],"representation":[106],"learning":[107,128],"well":[109],"meta-Path-based":[111],"relational":[112,178],"modeling":[113,179],"accurately":[115],"capture":[116],"relationships":[118],"between":[119],"Specifically,":[123],"design":[125,157],"multi-view":[127],"frame-work":[129],"that":[130],"integrates":[131],"latent":[132],"information":[133],"from":[134],"both":[135],"western":[138],"medicine":[139],"views.":[140],"We":[141,156],"incorporate":[142],"structures":[145],"herbal":[147],"compounds":[148],"target":[150,172],"proteins":[151],"better":[153],"characterize":[154],"features.":[155],"hypergraph":[159],"convolutional":[160],"network":[161],"model":[163],"high-order":[164],"relations":[165],"among":[166],"compounds,":[169],"diseases":[170],"proteins.":[173],"Moreover,":[174],"employ":[176],"meta-path-based":[177],"make":[181],"results":[183],"interpretable.":[184],"Extensive":[185],"experiments":[186],"on":[187],"real-world":[188],"datasets":[189],"demonstrate":[190],"effectiveness":[192],"proposed":[195],"model.":[196]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-30T00:00:00"}
