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Integrating Clinical Laboratory Measures and ICD-9 Code Diagnoses in Phenome-Wide Association Studies.

A. Verma, J. Leader, S. Verma, A. Frase, J. Wallace, S. Dudek, D. Lavage, C. Hout, F. Dewey, J. Penn, A. Lopez, J. Overton, D. Carey, D. Ledbetter, H. Kirchner, M. Ritchie, and S. Pendergrass. PSB, page 168-179. (2016)

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S Verma

Shefali Kumar

Damian Verma

Melanie Verma

Smita Verma

Other publications of authors with the same name

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.S. Verma, A. Lucas, D. Lavage, J. Leader, R. Metpally, S. Krishnamurthy, F. Dewey, I. Borecki, A. Lopez, J. Overton and 5 other author(s). PSB, page 533-544. (2017)Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies.B. Li, Y. Veturi, Y. Bradford, S. Verma, A. Verma, A. Lucas, D. Haas, and M. Ritchie. PSB, page 296-307. (2019)Session Introduction.M. Hall, S. Verma, D. Wall, J. Moore, B. Keating, D. Campbell, G. Gibson, F. Asselbergs, and S. Pendergrass. Pacific Symposium on Biocomputing, page 156-160. (2015)Neural network-based multiomics data integration in Alzheimer's disease.P. Singhal, S. Verma, S. Dudek, and M. Ritchie. GECCO (Companion), page 403-404. ACM, (2019)Biofilter as a Functional Annotation Pipeline for Common and Rare Copy Number Burden.D. Kim, A. Lucas, J. Glessner, S. Verma, Y. Bradford, R. Li, A. Frase, H. Hakonarson, P. Peissig, M. Brilliant and 1 other author(s). PSB, page 357-368. (2016)Integrating Clinical Laboratory Measures and ICD-9 Code Diagnoses in Phenome-Wide Association Studies.A. Verma, J. Leader, S. Verma, A. Frase, J. Wallace, S. Dudek, D. Lavage, C. Hout, F. Dewey, J. Penn and 7 other author(s). PSB, page 168-179. (2016)Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression.B. Li, S. Verma, Y. Veturi, A. Verma, Y. Bradford, D. Haas, and M. Ritchie. PSB, page 448-459. (2018)Phenome-Wide Interaction Study (PheWIS) in Aids Clinical Trials Group Data (ACTG).S. Verma, A. Frase, A. Verma, S. Pendergrass, S. Mahony, D. Haas, and M. Ritchie. PSB, page 57-68. (2016)Next-Generation Analysis of Cataracts: Determining Knowledge Driven Gene-Gene Interactions Using Biofilter, and Gene-Environment Interactions Using the PhenX Toolkit.S. Pendergrass, S. Verma, E. Holzinger, C. Moore, J. Wallace, S. Dudek, W. Huggins, T. Kitchner, C. Waudby, R. Berg and 2 other author(s). Pacific Symposium on Biocomputing, page 147-158. World Scientific Publishing, (2013)Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma.D. Kim, R. Li, A. Lucas, S. Verma, S. Dudek, and M. Ritchie. JAMIA, 24 (3): 577-587 (2017)

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Disambiguation of "Verma, Shefali S."

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Integrating Clinical Laboratory Measures and ICD-9 Code Diagnoses in Phenome-Wide Association Studies.

Please choose a person to relate this publication to

S Verma

Shefali Kumar

Damian Verma

Melanie Verma

Smita Verma

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Verma, Shefali S."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Integrating Clinical Laboratory Measures and ICD-9 Code Diagnoses in Phenome-Wide Association Studies.

Please choose a person to relate this publication to

S Verma

Shefali Kumar

Damian Verma

Melanie Verma

Smita Verma

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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MSOffice XML

Integrating Clinical Laboratory Measures and ICD-9 Code Diagnoses in Phenome-Wide Association Studies.