2017
Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases
Raja K, Patrick M, Elder J, Tsoi L. Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases. Scientific Reports 2017, 7: 3690. PMID: 28623363, PMCID: PMC5473874, DOI: 10.1038/s41598-017-03914-3.Peer-Reviewed Original ResearchConceptsDrug-gene interactionsDDI corpusPrediction of adverse drug reactionsRandom forest classifierMachine learning workflowPrediction of drug-drug interactionsF-scoreDrug-drug interactionsAdverse drug reactionsClassification modelMolecular levelLearning workflowForest classifierAdverse Drug Reactions ClassificationDrug discoveryClassifierADR typesCutaneous diseasePrevent adverse drug reactionsDrug reactionsPace of drug discoveryClassificationPotential drug-drug interactions
2016
Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature
Zhang Y, Wu H, Xu J, Wang J, Soysal E, Li L, Xu H. Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature. BMC Systems Biology 2016, 10: 67. PMID: 27585838, PMCID: PMC5009562, DOI: 10.1186/s12918-016-0311-2.Peer-Reviewed Original ResearchConceptsPaths graph kernelGraph kernelsSemantic classesSemantic informationBiomedical literatureShallow semantic representationsText mining techniquesBest F-scoreAutomatic DDI extractionProblem of sparsenessDependency structureSemantic graphDDI detectionKnowledge basesDDI corpusF-scoreDDI extractionSemantic representationNovel approachExperimental resultsKernelHigh precisionInformationSparsenessGraph
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