Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling
Lee H, Emani P, Gerstein M. Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling. Journal Of Chemical Information And Modeling 2024 PMID: 39576762, DOI: 10.1021/acs.jcim.4c01116.Peer-Reviewed Original ResearchBinding affinity predictionAffinity predictionMeta-modelMeta-modeling approachLigand-protein binding affinityState-of-the-art deep learning toolsState-of-the-artBinding affinityDeep learning modelsDeep learning toolsMolecular descriptorsInclusion of featuresVirtual screeningBase modelDatabase scalabilityGeneralization capabilityDiverse modeling approachesTraining databaseApplication benchmarksDrug ligandsLearning modelsLigandPhysicochemical propertiesLearning toolsDevelopment effortsStrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records
Lee H, Schwamm L, Sansing L, Kamel H, de Havenon A, Turner A, Sheth K, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. Npj Digital Medicine 2024, 7: 130. PMID: 38760474, PMCID: PMC11101464, DOI: 10.1038/s41746-024-01120-w.Peer-Reviewed Original ResearchElectronic health recordsWeighted F1MIMIC-IIIClinical decision support systemsMulti-class classificationNatural language processingMIMIC-III datasetHealth recordsMachine learning classifiersDecision support systemArtificial intelligence toolsVascular neurologistsLearning classifiersBinary classificationCross-validation accuracyLanguage processingMeta-modelIntelligence toolsStroke prevention effortsAcute ischemic strokeStroke etiologySupport systemStroke etiology classificationClassification toolClassifier