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Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression., , , , , , and . PSB, page 448-459. (2018)Biofilter as a Functional Annotation Pipeline for Common and Rare Copy Number Burden., , , , , , , , , and 1 other author(s). PSB, page 357-368. (2016)Integrating Clinical Laboratory Measures and ICD-9 Code Diagnoses in Phenome-Wide Association Studies., , , , , , , , , and 7 other author(s). PSB, page 168-179. (2016)Neural network-based multiomics data integration in Alzheimer's disease., , , and . GECCO (Companion), page 403-404. ACM, (2019)Phenome-Wide Interaction Study (PheWIS) in Aids Clinical Trials Group Data (ACTG)., , , , , , and . PSB, page 57-68. (2016)Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)., , , , , , , , , and 9 other author(s). BioData Min., (2015)Leveraging electronic health record data for endometriosis research., , , , , and . Frontiers Digit. Health, (May 2023)Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies., , , , , , , and . PSB, page 296-307. (2019)Identifying Genetic Associations with Variability in Metabolic Health and Blood Count Laboratory Values: Diving into the Quantitative Traits by Leveraging Longitudinal Data from an EHR., , , , , , , , , and 5 other author(s). PSB, page 533-544. (2017)A simulation study investigating power estimates in phenome-wide association studies., , , , , , and . BMC Bioinform., 19 (1): 120:1-120:8 (2018)