Dr. Eran Halperin is a professor in the departments of Computer Science, Computational Medicine, Anesthesiology, and Human Genetics. He is also the associate director of informatics in the Institute of Precision Health at UCLA and the co-director of the Computation Genomics Summer Institute at UCLA. Dr. Halperin received his Ph.D. in computer science from Tel-Aviv University. Prior to his current position, he held research and postdoctoral positions at the University of California, Berkeley, the International Computer Science Institute in Berkeley, Princeton University, and Tel-Aviv University.
Dr. Halperin is a computational biologist who develops statistical and computational methods for the analysis of human genetic and epigenetic variation in the context of complex human diseases. His group has developed methods and software that have been used by hundreds of researchers worldwide to understand the genetic causes of diseases such as cardiovascular diseases, non-Hodgkin's lymphoma, and breast cancer.
Dr. Halperin has published over 100 peer-reviewed articles across different disciplines such as human genetics, computational biology, and theoretical computer science. He received various honors for academic achievements, including the Rothschild Fellowship, the Technion-Juludan prize, and the Krill Prize.
The research in our lab mainly focuses on the development of computational tools for the analysis of genetic data; we are mostly interested in the development of tools that enable and facilitate genetic and epigenetic studies of common complex diseases, such as cancer, rheumatoid arthritis, or cardiovascular diseases. These studies shed important light on the biological mechanisms of these diseases, and they will pave the way to improved diagnosis and a personalize treatment based on an individual's genetics.
In more technical terms, we develop methods for the analysis of population genetics data (e.g., genome-wide association studies), epigenomics data (e.g., epigenome-wide association studies), and other omics data of populations. Our main motivation is the development of these methods in order to improve and facilitate studies of complex diseases. In our search for improved methodology we also consider general problems about the population. For example, we characterized methylation differences between men and women, we developed methods for the inference of ancestry from genomics data, we showed that ancestry is a key factor in choice of mates, we helped characterizing the history of the Jewish people based on population genetic data, and we explore the potential risks for privacy when DNA data is shared in public databases.
In addition, we collaborate with groups around the world in order to study specific diseases. Particularly, we have been working on genetic and epigenetic studies of Non-Hodgkin lymphoma, leukemia, age relaetd macular degeneration, rheumatoid arthritis, coronary artery disease, myocardial infarction (heart attack), and other cardiovascular measurements.
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