影响因子：9.5
发表刊物：Briefings in Bioinformatics
关键字：essential gene prediction; large language model; graph neural network; adversarial training; biological interpretation
摘要：Theidentificationandcharacterizationofessentialgenesarecentraltoourunderstandingofthecorebiologicalfunctionsineukaryoticorganisms,andhasimportantimplicationsforthetreatmentofdiseasescausedby,forexample,cancersandpathogens.Giventhemajorconstraintsintestingthefunctionsofgenesofmanyorganismsinthelaboratory,duetotheabsenceofinvitroculturesand/orgeneperturbationassaysformostmetazoanspecies,therehasbeenaneedtodevelopinsilicotoolsfortheaccuratepredictionorinferenceofessentialgenestounderpinsystemsbiologicalinvestigations.Majoradvancesinmachinelearningapproachesprovideunprecedentedopportunitiestoovercometheselimitationsandacceleratethediscoveryofessentialgenesonagenome-widescale.Here,wedevelopedandevaluatedalargelanguagemodel-andgraphneuralnetwork(LLM–GNN)-basedapproach,called‘Bingo’,topredictessentialprotein-codinggenesinthemetazoanmodelorganismsCaenorhabditiselegansandDrosophilamelanogasteraswellasinMusmusculusandHomosapiens(aHepG2cellline)byintegratingLLMandGNNswithadversarialtraining.Bingopredictsessentialgenesundertwo‘zero-shot’scenarioswithtransferlearning,showingpromisetocompensateforalackofhigh-qualitygenomicandproteomicdatafornon-modelorganisms.Inaddition,theattentionmechanismsandGNNExplainerwereemployedtomanifestthefunctionalsitesandstructuraldomainwithmostcontributiontoessentiality.Inconclusion,Bingoprovidestheprospectofbeingabletoaccuratelyinfertheessentialgenesoflittle-orunder-studiedorganismsofinterest,andprovidesabiologicalexplanationforgeneessentiality.
论文类型：期刊论文
文献类型：J
是否译文：否
发表时间：2024-01-12
收录刊物：SCI