2024
Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data
Lu Y, Tong J, Chubak J, Lumley T, Hubbard R, Xu H, Chen Y. Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data. Journal Of Biomedical Informatics 2024, 157: 104690. PMID: 39004110, DOI: 10.1016/j.jbi.2024.104690.Peer-Reviewed Original ResearchElectronic health recordsElectronic health record dataKaiser Permanente WashingtonEHR-derived phenotypesAssociation studiesHealth recordsColon cancer recurrencePhenotyping errorsComputable phenotypeRisk factorsCancer recurrenceMultiple phenotypesReduce biasImprove estimation accuracySimulation studyBias reductionKaiserReduction of biasBiasEstimation accuracyAssociationStudyOutcomesRiskEstimation efficiency
2015
A comparative study of disease genes and drug targets in the human protein interactome
Sun J, Zhu K, Zheng W, Xu H. A comparative study of disease genes and drug targets in the human protein interactome. BMC Bioinformatics 2015, 16: s1. PMID: 25861037, PMCID: PMC4402590, DOI: 10.1186/1471-2105-16-s5-s1.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesDisease genesDrug targetsHuman protein-coding genesHuman protein-protein interaction networkProtein-protein interaction networkProtein-coding genesHuman protein interactomeComplex diseasesNovel drug targetsProtein interactomeAnatomical Therapeutic Chemical (ATC) classificationInteraction networksDisease proteinAssociation studiesGenesDisease categoriesInteractomeProteinMajor disease categoriesDifferent disease categoriesFirst comprehensive comparisonTargetTreatment efficacyHigh betweenness