Sarah A. Pendergrass

发表

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

Marylyn D. Ritchie, Shefali S. Verma, Anurag Verma, 2016, PSB.

Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors

Jiang Gui, Jing Li, Jason H. Moore, 2014, BioData Mining.

Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis

Marylyn D. Ritchie, Dana C. Crawford, Scott M. Dudek, 2010, BioData Mining.

Microwave imaging for thermal therapy monitoring: temperature accuracy and image reconstruction time improvements

Paul M. Meaney, Keith D. Paulsen, Margaret W. Fanning, 2003, SPIE BiOS.

Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico

Xinyuan Zhang, Marylyn D. Ritchie, Sarah A. Pendergrass, 2018, BMC Bioinformatics.

Characterizing and Managing Missing Structured Data in Electronic Health Records

Brett K. Beaulieu-Jones, John W. Snyder, Daniel R. Lavage, 2017 .

The detection and characterization of pleiotropy: discovery, progress, and promise

Dana C. Crawford, Sarah A. Pendergrass, Anna L. Tyler, 2016, Briefings Bioinform..

ATHENA: the analysis tool for heritable and environmental network associations

Marylyn D. Ritchie, Scott M. Dudek, Alex T. Frase, 2014, Bioinform..

Visual Integration of Results from a Large Dna Biobank (Biovu) Using Synthesis-View

Marylyn D. Ritchie, Dana C. Crawford, Scott M. Dudek, 2011, Pacific Symposium on Biocomputing.

Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development

Marylyn D. Ritchie, John R. Wallace, Alex T. Frase, 2013, BioData Mining.

Contrasting Association Results Between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes

Marylyn D. Ritchie, Anurag Verma, Joseph B. Leader, 2015, AMIA.

Investigating the relationship between mitochondrial genetic variation and cardiovascular-related traits to develop a framework for mitochondrial phenome-wide association studies

Eric Farber-Eger, Dana C. Crawford, William S. Bush, 2014, BioData Mining.

Next-Generation Analysis of Cataracts: Determining Knowledge Driven Gene-Gene Interactions Using Biofilter, and Gene-Environment Interactions Using the PhenX Toolkit

Marylyn D. Ritchie, Shefali S. Verma, Scott M. Dudek, 2012, Pacific Symposium on Biocomputing.

Uncovering the Etiology of Autism Spectrum Disorders: Genomics, Bioinformatics, Environment, Data Collection and Exploration, and Future Possibilities

Sarah A. Pendergrass, Scott B. Selleck, Santhosh Girirajan, 2013, Pacific Symposium on Biocomputing.

Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View

Marylyn D. Ritchie, Dana C. Crawford, Scott M. Dudek, 2012, BioData Mining.

A simulation study investigating power estimates in phenome-wide association studies

Marylyn D. Ritchie, Yukiko Bradford, Shefali S. Verma, 2017, BMC Bioinformatics.

CLINICAL MICROWAVE BREAST IMAGING: EARLY RESULTS - WORK IN PROGRESS

Paul M. Meaney, Keith D. Paulsen, Steven P. Poplack, 2004 .

Using BioBin to Explore Rare Variant Population Stratification

Marylyn D. Ritchie, John R. Wallace, Alex T. Frase, 2012, Pacific Symposium on Biocomputing.

ERRATUM: NEXT-GENERATION ANALYSIS OF CATARACTS: DETERMINING KNOWLEDGE DRIVEN GENE-GENE INTERACTIONS USING BIOFILTER, AND GENE-ENVIRONMENT INTERACTIONS USING THE PHENX TOOLKIT.

Marylyn D. Ritchie, Scott M. Dudek, John R. Wallace, 2015, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

Microwave breast imaging with an under-determined reconstruction parameter mesh

Paul M. Meaney, Keith D. Paulsen, Qianqian Fang, 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

A Biologically Informed Method for Detecting Associations with Rare Variants

Marylyn D. Ritchie, Eric Torstenson, John R. Wallace, 2012, EvoBIO.

Learning Phenotype Mapping for Integrating Large Genetic Data

Marylyn D. Ritchie, José Luis Ambite, Chun-Nan Hsu, 2011, BioNLP@ACL.

Pathway analysis by randomization incorporating structure - PARIS: an update

Marylyn D. Ritchie, Scott M. Dudek, Alex T. Frase, 2016, Bioinform..

A simulation study investigating power estimates in Phenome-Wide Association Studies

Marylyn D. Ritchie, Anurag Verma, Scott M. Dudek, 2017 .

Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network

Xinyuan Zhang, Marylyn D. Ritchie, Chunhua Weng, 2018, PSB.

Knowledge-driven genomic interactions: an application in ovarian cancer

Marylyn D. Ritchie, Scott M. Dudek, Do Kyoon Kim, 2014, BioData Mining.

Phenome-Wide Interaction Study (PheWIS) in Aids Clinical Trials Group Data (ACTG)

Marylyn D. Ritchie, Shefali S. Verma, Anurag Verma, 2016, PSB.

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

Marylyn D. Ritchie, Shefali S. Verma, Joseph B. Leader, 2017, PSB.

Visualizing genomic information across chromosomes with PhenoGram

Marylyn D. Ritchie, Scott M. Dudek, Sarah A. Pendergrass, 2013, BioData Mining.

Interpretation of machine learning predictions for patient outcomes in electronic health records

Jason H. Moore, William La Cava, Sarah A. Pendergrass, 2019, AMIA.

Towards a phenome-wide catalog of human clinical traits impacted by genetic ancestry

Nicole A. Restrepo, Eric Farber-Eger, Dana C. Crawford, 2015, BioData Mining.

Spectrum analysis of microwave breast examination data and reconstructed images

Paul M. Meaney, Qianqian Fang, Sarah A. Pendergrass, 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

Opening the Door to the Large Scale Use of Clinical Lab Measures for Association Testing: Exploring Different Methods for Defining Phenotypes

Joseph B. Leader, Daniel R. Lavage, Sarah A. Pendergrass, 2017, PSB.

Low Frequency Variants, Collapsed Based on Biological Knowledge, Uncover Complexity of Population Stratification in 1000 Genomes Project Data

Marylyn D. Ritchie, Alex T. Frase, Sarah A. Pendergrass, 2013, PLoS genetics.

A broadband microwave breast imaging system

Paul M. Meaney, Keith D. Paulsen, Margaret W. Fanning, 2003, 2003 IEEE 29th Annual Proceedings of Bioengineering Conference.

Limited Systemic Sclerosis Patients with Pulmonary Arterial Hypertension Show Biomarkers of Inflammation and Vascular Injury

Sarah A. Pendergrass, Michael L. Whitfield, S. Pendergrass, 2010, PloS one.

Parallel-detection microwave spectroscopy system for breast imaging

Paul M. Meaney, Keith D. Paulsen, Margaret W. Fanning, 2004 .

Phenome-Wide Association Study to Explore Relationships between Immune System Related Genetic Loci and Complex Traits and Diseases

Gerard Tromp, Marylyn D. Ritchie, Anurag Verma, 2016, PloS one.

eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants

Suzette J. Bielinski, Gerard Tromp, Marylyn D. Ritchie, 2016, BMC Medical Genomics.

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study

Marylyn D. Ritchie, Scott Mellis, Alan R. Shuldiner, 2016, Science.

A Core MYC Gene Expression Signature Is Prominent in Basal-Like Breast Cancer but Only Partially Overlaps the Core Serum Response

Sarah A. Pendergrass, Charles M. Perou, Michael L. Whitfield, 2009, PloS one.

Molecular Subsets in the Gene Expression Signatures of Scleroderma Skin

Sarah A. Pendergrass, Michael L. Whitfield, S. Pendergrass, 2008, PloS one.

Detection of Pleiotropy through a Phenome-Wide Association Study (PheWAS) of Epidemiologic Data as Part of the Environmental Architecture for Genes Linked to Environment (EAGLE) Study

Marylyn D. Ritchie, Dana C. Crawford, Anurag Verma, 2014, PLoS genetics.

Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index

Suzette J. Bielinski, Gerard Tromp, Christopher G. Chute, 2014, Front. Genet..

Phenome-Wide Association Study (PheWAS) for Detection of Pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network

Marylyn D. Ritchie, Dana C. Crawford, Ewa Deelman, 2013, PLoS genetics.

Multiphenotype association study of patients randomized to initiate antiretroviral regimens in AIDS Clinical Trials Group protocol A5202

Marylyn D. Ritchie, Shefali S. Verma, Anurag Verma, 2017, Pharmacogenetics and genomics.