Predictive Modeling and Personalized Medicine
We have a variety of projects ranging from brain mapping to derive optimal brain atlases, integrated omic analyses to identify genetic underpinnings of the brain, to precision medicine approaches for drug response prediction and drug target identification.
Dr. Tsung-Ting Kuo is an Assistant Professor of Medicine in University of California San Diego (UCSD) Health Department of Biomedical Informatics (DBMI). He is mainly conducting biomedical, healthcare and genomic studies based on blockchain and predictive modeling. His research focuses on blockchain technologies, machine learning, and natural language processing.
Our goal is to identify genes causing insulin resistance in humans in order to find new therapeutic targets for diabetes and cardiometabolic diseases. Our approach to discovery is grounded in human genetics, clarified through systematic, high throughput experimentation in human cells, and calibrated by its relevance to clinical disease. We use massively parallel genome engineering to re-create mutations identified in patients and develop high-throughput assays to interrogate function in human cell models. We apply bioinformatics and statistics to make sense of this data integrating 1) human mutations, 2) cellular function, and 3) metabolic/glycemic phenotypes of the individuals who harbor them. Using this approach, we have discovered novel missense mutations that greatly increase risk for type 2 diabetes. As a complementary aim towards precision medicine, we develop tools for clinical genome interpretation powered by high-throughput experimental data.
The main objective of the Chavez laboratory is the molecular characterization of malignant childhood cancers in order to identify drug targets and improve treatment options. Our focus is mainly on pediatric brain tumors such as medulloblastoma, glioblastoma, and ependymoma. Recently, we have demonstrated how to leverage epigenetic information such as DNA methylation and enhancer profiling in pediatric brain tumors and normal human tissues to identify clinically relevant tumor subgroups, oncogenic enhancers, transcription factors, and pathways amenable to pharmacologic targeting. To reveal regulatory circuitries disturbed in childhood brain tumors, we generate and integrate public high-dimensional data from primary tumors and patient-derived cell lines. We are specifically interested in the analysis of somatic and germline DNA mutations, chromatin and DNA modifications, transcription factor binding, and gene expression.
Dr. Koola is a physician scientist specializing in Biomedical Informatics and Hospital Medicine. He specializes in the area of big data machine learning for predictive analytics. In particular, he is interested in using electronic health records to improve care delivery--particularly for patients with advanced liver disease. Using risk prediction models in a healthcare context requires understanding of: (i) the healthcare system of intended use; (ii) risk model building; (iii) risk model assessment; and (iv) risk model re-calibration. Additionally, Dr. Koola is interested in visual analytics, data modeling, and health services research.
Research in the Mesirov Lab focuses on cancer genomics applying machine-learning methods to functional data derived from patient tumors. The lab analyzes these molecular data to determine the underlying biological mechanisms of specific tumor subtypes, to stratify patients according to their relative risks of relapse, and to identify candidate compounds for new treatments. The overall goal is to treat patients as individuals specific to their tumors. Importantly, the lab is committed to the development of practical, accessible software tools to bring these methods to the general biomedical research community.