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 blockchain-based biomedical, healthcare and genomic studies. His research focuses on blockchain technologies, machine learning, and natural language processing.
Predictive Modeling and Personalized Medicine
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.
My research focuses on solving natural language processing problems in biomedical sciences (bioNLP).