2020
BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale
Chen Q, Lee K, Yan S, Kim S, Wei C, Lu Z. BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale. PLOS Computational Biology 2020, 16: e1007617. PMID: 32324731, PMCID: PMC7237030, DOI: 10.1371/journal.pcbi.1007617.Peer-Reviewed Original ResearchConceptsConcept embeddingsNER toolsLearning modelBiomedical text mining applicationsAdvanced deep learning modelsDifferent machine learning modelsEvaluation resultsText mining applicationsDeep learning modelsSemantics of conceptsMachine learning modelsLiterature-based discoveryConcept recognitionDifferent machineProtein-protein interaction predictionPubMed abstractsRecognition toolsMassive numberVector representationBiomedical conceptsLarge marginExtrinsic evaluationBiomedical literatureIntrinsic evaluationSemantic relatedness
2019
ML-Net: multi-label classification of biomedical texts with deep neural networks
Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z. ML-Net: multi-label classification of biomedical texts with deep neural networks. Journal Of The American Medical Informatics Association 2019, 26: 1279-1285. PMID: 31233120, PMCID: PMC7647240, DOI: 10.1093/jamia/ocz085.Peer-Reviewed Original ResearchMeSH KeywordsBenchmarkingClassificationComputational BiologyData MiningDeep LearningMachine LearningNatural Language ProcessingNeural Networks, ComputerConceptsMulti-label classificationML-NetBiomedical textEnd deep learning frameworkMulti-label text classificationDeep learning frameworkDeep neural networksTraditional machineDocument contextFeature engineeringText classificationTextual documentsMachine learningNovel endLearning frameworkPrediction networkIndividual classifiersNeural networkHuman effortTarget documentsF-measureArt methodsPrediction mechanismContextual informationLabel countsOverview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine
Doğan R, Kim S, Chatr-aryamontri A, Wei C, Comeau D, Antunes R, Matos S, Chen Q, Elangovan A, Panyam N, Verspoor K, Liu H, Wang Y, Liu Z, Altınel B, Hüsünbeyi Z, Özgür A, Fergadis A, Wang C, Dai H, Tran T, Kavuluru R, Luo L, Steppi A, Zhang J, Qu J, Lu Z. Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine. Database 2019, 2019: bay147. PMID: 30689846, PMCID: PMC6348314, DOI: 10.1093/database/bay147.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyData MiningDatabases, ProteinHumansMutationPrecision MedicineProtein Interaction MappingProtein Interaction MapsSoftwareConceptsRelation extraction taskDocument triage taskBest F-scoreExtraction taskTriage taskKnowledge basesF-scorePubMed documentsArt deep learning methodsText-mining research communityLarge knowledge basesDeep learning methodsText mining systemText mining modelText mining toolsBest average precisionData setsLarge-scale corpusHuman annotationsElectronic health recordsSystem developersBetter recallText miningAverage precisionLearning methods