Assistant Professor in the Department of Biomedical Informatics
Associate Director of the Biomedical Informatics Training Program
|Post-Doctoral||Computational Biology||National Institute on Aging in 2006|
|PhD||Computer Science||University of Maryland in 2003|
|MS||Electrical Engineering and Computer Science||Massachusetts Institute of Technology in 1991|
|BS||Computer Science and Engineering||Massachusetts Institute of Technology in 1991|
2012 - present
|Assistant Professor, Intelligent Systems Program, Kenneth P. Dietrich School of Arts and Sciences, University of Pittsburgh|
2009 - present
|Assistant Professor, Biomedical Informatics, Medicine, University of Pittsburgh|
|09/15/2014 - 08/31/2018|
Center for Causal Modeling and Discovery of Biomedical Knowledge from Big Data
|08/1/2014 - 07/31/2015|
Supplement to Supplement to Semantic LAMHDI: Linking diseases to model organism resources
|05/1/2014 - 04/30/2019|
NIH/NCI 1 U24 CA184407-01
Cancer Deep Phenotype Extraction from Electronic Medical Records
|05/1/2014 - 04/30/2019|
Human Genomics Analysis Interface for FaceBase 2
|02/1/2014 - 02/1/2018|
Baobab Health Trust (CDC) U2GGH00729
Improving the delivery & management of HIV /AIDS care in Malawi through Appropriate Medical Informatics
|02/1/2014 - 01/31/2017|
NIH/NLM 1 R01 LM011838-01
Addressing gaps in clinically useful evidence on drug-drug interactions
|09/1/2012 - 06/30/2016|
Oregon Health Sciences University (NIH) 1R24 OD011883
Semantic LAMHDI: Linking diseases to model organism resources
|07/1/2012 - 06/30/2016|
Quantifying Electronic Medical Records Usability to Improve Clinical Workflow
|04/1/2012 - 03/31/2015|
Sarcoidosis and A1AT Genomics & Informatics Center
|07/1/2011 - 08/31/2015|
Interactive Search and Review of Clinical Records with Multi-layered Semantic Annotation
Abstract: As key experts in supporting medication-decision making, pharmacists are well-positioned to support the incorporation of pharmacogenomics into clinical care. However, there has been little study to date of pharmacists' information needs regarding pharmacogenomics. Understanding those needs is critical to design information resources that help pharmacists effectively apply pharmacogenomics information.
Abstract: The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.
Abstract: The Matchmaker Exchange application programming interface (API) allows searching a patient's genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enable matching known diseases and variant-phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases.
Abstract: Sarcoidosis is a systemic disease characterized by noncaseating granulomatous inflammation with tremendous clinical heterogeneity and uncertain pathobiology and lacking in clinically useful biomarkers. The Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study is an observational cohort study designed to explore the role of the lung microbiome and genome in these two diseases. This article describes the design and rationale for the GRADS study sarcoidosis protocol. The study addresses the hypothesis that distinct patterns in the lung microbiome are characteristic of sarcoidosis phenotypes and are reflected in changes in systemic inflammatory responses as measured by peripheral blood changes in gene transcription. The goal is to enroll 400 participants, with a minimum of 35 in each of 9 clinical phenotype subgroups prioritized by their clinical relevance to understanding of the pathobiology and clinical heterogeneity of sarcoidosis. Participants with a confirmed diagnosis of sarcoidosis undergo a baseline visit with self-administered questionnaires, chest computed tomography, pulmonary function tests, and blood and urine testing. A research or clinical bronchoscopy with a research bronchoalveolar lavage will be performed to obtain samples for genomic and microbiome analyses. Comparisons will be made by blood genomic analysis and with clinical phenotypic variables. A 6-month follow-up visit is planned to assess each participant's clinical course. By the use of an integrative approach to the analysis of the microbiome and genome in selected clinical phenotypes, the GRADS study is powerfully positioned to inform and direct studies on the pathobiology of sarcoidosis, identify diagnostic or prognostic biomarkers, and provide novel molecular phenotypes that could lead to improved personalized approaches to therapy for sarcoidosis.
Abstract: Severe deficiency of alpha-1 antitrypsin has a highly variable clinical presentation. The Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis α1 Study is a prospective, multicenter, cross-sectional study of adults older than age 35 years with PiZZ or PiMZ alpha-1 antitrypsin genotypes. It is designed to better understand if microbial factors influence this heterogeneity. Clinical symptoms, pulmonary function testing, computed chest tomography, exercise capacity, and bronchoalveolar lavage (BAL) will be used to define chronic obstructive pulmonary disease (COPD) phenotypes that can be studied with an integrated systems biology approach that includes plasma proteomics; mouth, BAL, and stool microbiome and virome analysis; and blood microRNA and blood mononuclear cell RNA and DNA profiling. We will rely on global genome, transcriptome, proteome, and metabolome datasets. Matched cohorts of PiZZ participants on or off alpha-1 antitrypsin augmentation therapy, PiMZ participants not on augmentation therapy, and control participants from the Subpopulations and Intermediate Outcome Measures in COPD Study who match on FEV1 and age will be compared. In the primary analysis, we will determine if the PiZZ individuals on augmentation therapy have a difference in lower respiratory tract microbes identified compared with matched PiZZ individuals who are not on augmentation therapy. By characterizing the microbiome in alpha-1 antitrypsin deficiency (AATD), we hope to define new phenotypes of COPD that explain some of the diversity of clinical presentations. As a unique genetic cause of COPD, AATD may inform typical COPD pathogenesis, and better understanding of it may illuminate the complex interplay between environment and genetics. Although the biologic approaches are hypothesis generating, the results may lead to development of novel biomarkers, better understanding of COPD phenotypes, and development of novel diagnostic and therapeutic trials in AATD and COPD. Clinical trial registered with www.clinicaltrials.gov (NCT01832220).
Abstract: New sequencing technologies have ushered in a new era for diagnosis and discovery of new causative mutations for rare diseases. However, the sheer numbers of candidate variants that require interpretation in an exome or genomic analysis are still a challenging prospect. A powerful approach is the comparison of the patient's set of phenotypes (phenotypic profile) to known phenotypic profiles caused by mutations in orthologous genes associated with these variants. The most abundant source of relevant data for this task is available through the efforts of the Mouse Genome Informatics group and the International Mouse Phenotyping Consortium. In this review, we highlight the challenges in comparing human clinical phenotypes with mouse phenotypes and some of the solutions that have been developed by members of the Monarch Initiative. These tools allow the identification of mouse models for known disease-gene associations that may otherwise have been overlooked as well as candidate genes may be prioritized for novel associations. The culmination of these efforts is the Exomiser software package that allows clinical researchers to analyse patient exomes in the context of variant frequency and predicted pathogenicity as well the phenotypic similarity of the patient to any given candidate orthologous gene.