{"id":"https://openalex.org/W4406261071","doi":"https://doi.org/10.1109/bibm62325.2024.10822611","title":"GRNNLink: Predicting gene regulatory links from single-cell RNA-seq data using graph recurrent neural network","display_name":"GRNNLink: Predicting gene regulatory links from single-cell RNA-seq data using graph recurrent neural network","publication_year":2024,"publication_date":"2024-12-03","ids":{"openalex":"https://openalex.org/W4406261071","doi":"https://doi.org/10.1109/bibm62325.2024.10822611"},"language":"en","primary_location":{"id":"doi:10.1109/bibm62325.2024.10822611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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/A5088685596","display_name":"Liang Bai","orcid":"https://orcid.org/0000-0002-7667-6539"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liang Bai","raw_affiliation_strings":["Hunan University,College of Computer Science and Electronic Engineering,Changsha,China,410082"],"affiliations":[{"raw_affiliation_string":"Hunan University,College of Computer Science and Electronic Engineering,Changsha,China,410082","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101485863","display_name":"Shulin Wang","orcid":"https://orcid.org/0000-0003-1474-6455"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shulin Wang","raw_affiliation_strings":["Hunan University,College of Computer Science and Electronic Engineering,Changsha,China,410082"],"affiliations":[{"raw_affiliation_string":"Hunan University,College of Computer Science and Electronic Engineering,Changsha,China,410082","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088685596"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":0.2501,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.61183471,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"491","last_page":"496"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.9866999983787537,"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":0.9866999983787537,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9542999863624573,"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/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9223999977111816,"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/rna-seq","display_name":"RNA-Seq","score":0.7776814699172974},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5797366499900818},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.48476117849349976},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4798969030380249},{"id":"https://openalex.org/keywords/gene-regulatory-network","display_name":"Gene regulatory network","score":0.45722025632858276},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4277942180633545},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.4078100621700287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3800630569458008},{"id":"https://openalex.org/keywords/gene-expression","display_name":"Gene expression","score":0.26334142684936523},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.23169603943824768},{"id":"https://openalex.org/keywords/genetics","display_name":"Genetics","score":0.17581439018249512},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.17235103249549866},{"id":"https://openalex.org/keywords/transcriptome","display_name":"Transcriptome","score":0.13753995299339294}],"concepts":[{"id":"https://openalex.org/C107397762","wikidata":"https://www.wikidata.org/wiki/Q2542347","display_name":"RNA-Seq","level":5,"score":0.7776814699172974},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5797366499900818},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.48476117849349976},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4798969030380249},{"id":"https://openalex.org/C67339327","wikidata":"https://www.wikidata.org/wiki/Q1502576","display_name":"Gene regulatory network","level":4,"score":0.45722025632858276},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4277942180633545},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.4078100621700287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3800630569458008},{"id":"https://openalex.org/C150194340","wikidata":"https://www.wikidata.org/wiki/Q26972","display_name":"Gene expression","level":3,"score":0.26334142684936523},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.23169603943824768},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.17581439018249512},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.17235103249549866},{"id":"https://openalex.org/C162317418","wikidata":"https://www.wikidata.org/wiki/Q252857","display_name":"Transcriptome","level":4,"score":0.13753995299339294}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm62325.2024.10822611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W2076513103","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/W2803928488","https://openalex.org/W2900569176","https://openalex.org/W2904574059","https://openalex.org/W2951424836","https://openalex.org/W2964315448","https://openalex.org/W2996140569","https://openalex.org/W2997831639","https://openalex.org/W2998917483","https://openalex.org/W3045910546","https://openalex.org/W3127245931","https://openalex.org/W3193795275","https://openalex.org/W4200379876","https://openalex.org/W4207048141","https://openalex.org/W4221083556","https://openalex.org/W4229015556","https://openalex.org/W4281778744","https://openalex.org/W4288384137","https://openalex.org/W4290787198","https://openalex.org/W4290930137","https://openalex.org/W4294991206","https://openalex.org/W4297457819","https://openalex.org/W4308155906","https://openalex.org/W4309958134","https://openalex.org/W4311904335","https://openalex.org/W4312058912","https://openalex.org/W4313545233","https://openalex.org/W4366498340","https://openalex.org/W4381929620","https://openalex.org/W4383531854","https://openalex.org/W4391172914","https://openalex.org/W4394570041"],"related_works":["https://openalex.org/W4394426846","https://openalex.org/W4200033037","https://openalex.org/W4394543623","https://openalex.org/W3213655218","https://openalex.org/W4394560977","https://openalex.org/W4394488504","https://openalex.org/W4394163355","https://openalex.org/W3147425560","https://openalex.org/W2233436997","https://openalex.org/W4394166743"],"abstract_inverted_index":{"Single-cell":[0],"RNA":[1],"sequencing":[2],"(scRNA-seq)":[3],"technology":[4],"offers":[5],"unprecedented":[6],"opportunities":[7],"for":[8,52],"inferring":[9],"gene":[10,113],"regulatory":[11,85],"networks":[12],"(GRNs)":[13],"at":[14],"the":[15,92,117,128],"genome":[16],"level.":[17],"However,":[18],"scRNA-seq":[19,53,94,150],"data":[20,63],"is":[21,125],"highly":[22],"sparse":[23],"and":[24,161],"has":[25],"a":[26,73,105],"low":[27],"signal-to-noise":[28],"ratio":[29],"with":[30,142],"significant":[31],"dropout.":[32],"Many":[33],"unsupervised":[34],"or":[35],"self-supervised":[36],"models":[37],"have":[38],"been":[39],"proposed":[40],"to":[41,82,111,132],"infer":[42,83],"GRNs":[43,81],"from":[44],"large":[45],"RNA-seq":[46],"datasets,":[47],"but":[48],"few":[49],"are":[50],"suitable":[51],"data.":[54,95],"Recent":[55],"research":[56],"confirms":[57],"that":[58,155],"transcription":[59],"factor":[60],"(TF)-DNA":[61],"binding":[62],"enables":[64],"supervised":[65],"GRN":[66,145],"inference.":[67],"In":[68],"this":[69],"paper,":[70],"we":[71,90,97,139],"propose":[72],"novel":[74],"framework":[75],"called":[76],"GRNNLink,":[77],"which":[78],"leverages":[79],"known":[80],"potential":[84],"relationships":[86],"between":[87,119],"genes.":[88],"First,":[89],"preprocess":[91],"raw":[93],"Then,":[96],"introduce":[98],"an":[99],"interactive":[100],"graph":[101,106],"encoder":[102],"based":[103],"on":[104],"recurrent":[107],"neural":[108],"network":[109,120],"(GRNN)":[110],"refine":[112],"features":[114,131],"by":[115],"capturing":[116],"correlations":[118],"nodes.":[121],"Finally,":[122],"matrix":[123],"completion":[124],"performed":[126],"using":[127],"node":[129],"correlation":[130],"predict":[133],"GRNs.":[134],"To":[135],"evaluate":[136],"model":[137],"performance,":[138],"compare":[140],"GRNNLink":[141],"six":[143],"existing":[144],"reconstruction":[146],"methods":[147],"across":[148],"seven":[149],"datasets.":[151],"The":[152],"results":[153],"demonstrate":[154],"our":[156],"method":[157],"exhibits":[158],"high":[159],"robustness":[160],"accuracy.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
