Biomedical Informatics to Gain
Insights into Pulmonary Conditions
Driven by Truly Collaborative Research.
The Himes Lab is part of the Department of Biostatistics, Epidemiology, and Informatics and the Institute for Biomedical Informatics at the University of Pennsylvania. We are a computational group whose overarching goal is to use biomedical informatics approaches to better understand complex respiratory diseases. We do so by leveraging large publicly available datasets to gain disease insights via the integration of diverse omics data types, as well as by enhancing Electronic Health Record (EHR) data with information on social, economic and environmental factors.
We collaborate with experts in pulmonary biology to experimentally validate our findings, collect novel genome-scale datasets, and help them analyze and interpret their own data. We share data and results with others whenever possible for the sake of transparency and to maximize the chances that our findings advance efforts by the entire scientific community.
We believe that the greatest insights into complex diseases arise from collaborative efforts involving experts in medicine, molecular biology, and computation. Honesty and integrity of all group members and collaborators is essential to help us achieve our goal of making discoveries that improve the lives of patients.
Areas of Research
Chronic respiratory disease genetics
Asthma and COPD are heritable conditions, and several genetic variants have been associated with their risk, as well as responses to drugs used in their treatment. We seek such variants and try to understand the biological mechanisms that underlie the associations using datasets from diverse populations.
Multi-omics data integration
Omics approaches have enabled the unbiased measure of multi-layered information in biological systems to address a wide range of biomedical questions. We use such approaches to understand tissue-specific mechanisms of chronic respiratory disease-related exposures and drug responses.
Population studies with diverse data types
Chronic respiratory diseases arise from the complex interplay of genetic, social, economic, and environmental factors. To understand these complex relationships, we use Electronic Health Records and epidemiologic study data enhanced with various complementary and publicly available datasets containing geospatially distributed information.
Get in Touch
We welcome questions about our research, teaching and other academic topics. Highly motivated individuals are encouraged to inquire about positions.
“The doors of wisdom are never shut.”