{"id":"https://openalex.org/W7126083816","doi":"https://doi.org/10.1109/bibm66473.2025.11357086","title":"GRN-LGT: Predicting GRN Links with Gene Language Model and Graph Transformer","display_name":"GRN-LGT: Predicting GRN Links with Gene Language Model and Graph Transformer","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126083816","doi":"https://doi.org/10.1109/bibm66473.2025.11357086"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11357086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357086","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/A5124234723","display_name":"Yanxin Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanxin Chen","raw_affiliation_strings":["School of Software Engineering, Southeast University,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software Engineering, Southeast University,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101582205","display_name":"Mi Yang","orcid":"https://orcid.org/0000-0001-7280-1497"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingjie Yang","raw_affiliation_strings":["School of Computer Science and Engineering, Southeast University,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Southeast University,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100430876","display_name":"Xi Zhang","orcid":"https://orcid.org/0000-0003-3415-5345"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Southeast University,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Southeast University,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.59828241,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"242","last_page":"247"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.5149000287055969,"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"}},"topics":[{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.5149000287055969,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.19220000505447388,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.08340000361204147,"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/encode","display_name":"ENCODE","score":0.6754000186920166},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.560699999332428},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5293999910354614},{"id":"https://openalex.org/keywords/gene-regulatory-network","display_name":"Gene regulatory network","score":0.5289999842643738},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.49239999055862427},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46059998869895935},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.42899999022483826},{"id":"https://openalex.org/keywords/gene-prediction","display_name":"Gene prediction","score":0.38940000534057617}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7045000195503235},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6754000186920166},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.560699999332428},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5293999910354614},{"id":"https://openalex.org/C67339327","wikidata":"https://www.wikidata.org/wiki/Q1502576","display_name":"Gene regulatory network","level":4,"score":0.5289999842643738},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47699999809265137},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.42899999022483826},{"id":"https://openalex.org/C105565629","wikidata":"https://www.wikidata.org/wiki/Q1248292","display_name":"Gene prediction","level":4,"score":0.38940000534057617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36410000920295715},{"id":"https://openalex.org/C2991772755","wikidata":"https://www.wikidata.org/wiki/Q751748","display_name":"Gene interaction","level":3,"score":0.350600004196167},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33329999446868896},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C28225019","wikidata":"https://www.wikidata.org/wiki/Q4915005","display_name":"Biological network","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C165864922","wikidata":"https://www.wikidata.org/wiki/Q411391","display_name":"Regulation of gene expression","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C207850805","wikidata":"https://www.wikidata.org/wiki/Q269608","display_name":"Reverse engineering","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.26910001039505005},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C195818886","wikidata":"https://www.wikidata.org/wiki/Q5421724","display_name":"Expressive power","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C152662350","wikidata":"https://www.wikidata.org/wiki/Q815297","display_name":"Systems biology","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11357086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357086","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/G5862890032","display_name":null,"funder_award_id":"32370597","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1574447377","https://openalex.org/W2092939357","https://openalex.org/W2126750365","https://openalex.org/W2140258573","https://openalex.org/W2465917013","https://openalex.org/W2561884319","https://openalex.org/W2626990934","https://openalex.org/W2757234574","https://openalex.org/W2789254933","https://openalex.org/W2900569176","https://openalex.org/W2951424836","https://openalex.org/W2964315448","https://openalex.org/W2996140569","https://openalex.org/W3045910546","https://openalex.org/W3127238141","https://openalex.org/W4281778744","https://openalex.org/W4290930137","https://openalex.org/W4378530717","https://openalex.org/W4387774515","https://openalex.org/W4388870182","https://openalex.org/W4406261071","https://openalex.org/W4414563965"],"related_works":[],"abstract_inverted_index":{"Gene":[0],"regulatory":[1,46,79],"networks":[2],"(GRNs)":[3],"depict":[4],"the":[5,53,75,132,145,167],"intri-cate":[6],"interactions":[7],"among":[8],"genes,":[9],"which":[10,43],"are":[11],"essential":[12],"for":[13,91],"under-standing":[14],"complex":[15],"cellular":[16],"biological":[17],"processes.":[18],"However,":[19],"predicting":[20],"potential":[21],"links":[22],"in":[23,49,149],"GRNs":[24,57],"presents":[25],"substantial":[26],"challenges.":[27],"A":[28],"key":[29],"limitation":[30],"of":[31,56,77],"current":[32],"approaches":[33],"is":[34,147,181],"their":[35],"reliance":[36],"solely":[37],"on":[38],"single-cell":[39,126],"gene":[40,61,98,113,127,141],"expression":[41,128],"data,":[42],"neglects":[44],"important":[45],"signals":[47],"encoded":[48],"DNA":[50],"sequences.":[51],"Furthermore,":[52],"sparse":[54],"nature":[55],"restricts":[58],"communication":[59],"between":[60,68],"nodes":[62],"and":[63,70,115,130,165],"introduces":[64],"a":[65,88,97,103,136,150],"significant":[66],"imbalance":[67],"known":[69,154],"unknown":[71],"links,":[72],"further":[73],"hindering":[74],"identification":[76],"novel":[78],"in-teractions.":[80],"To":[81],"overcome":[82],"these":[83,122],"limitations,":[84],"we":[85],"propose":[86],"GRN-LGT,":[87],"predictive":[89],"model":[90,100,110,146],"GRN":[92,155],"link":[93],"prediction":[94],"that":[95],"integrates":[96],"language":[99,109],"DeepGene":[101,108],"with":[102,125,153],"Graph":[104,137],"Transformer.":[105],"GRN-LGT":[106,161],"employs":[107],"to":[111,139,158],"encode":[112],"sequences":[114],"extract":[116],"sequence-level":[117],"features.":[118],"It":[119],"then":[120],"fuses":[121],"sequence-derived":[123],"features":[124],"features,":[129],"feeds":[131],"combined":[133],"representations":[134],"into":[135],"Transformer":[138],"generate":[140],"node":[142],"embeddings.":[143],"Finally,":[144],"trained":[148],"supervised":[151],"manner":[152],"links.":[156],"Compared":[157],"existing":[159],"methods,":[160],"demonstrates":[162],"greater":[163],"robustness":[164],"achieves":[166],"best":[168],"average":[169],"performance":[170],"across":[171],"44":[172],"downstream":[173],"tasks":[174],"encompassing":[175],"multi-species":[176],"datasets.":[177],"The":[178],"source":[179],"code":[180],"available":[182],"at":[183],"https://github.com/ChenYanxin99/GRN-LGT.git.":[184]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-30T00:00:00"}