Predictive Modeling and Personalized Medicine

Tsung-Ting Kuo

Faculty Status
Active
Title
Assistant Professor
Email
tskuo@ucsd.edu
Track(s)
Biomedical Informatics
Brief Research Description
Blockchain Technologies, Machine Learning, Natural Language Processing
Lab Description

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.

Shamim Nemati

Faculty Status
Active
Title
Assistant Professor
Email
snemati@health.ucsd.edu
Phone
(405) 850-4751
Track(s)
Biomedical Informatics
Brief Research Description
Signal Processing, Dynamical Systems, Multivariate Time Series, Point Process, Deep Learning, Reinforcement Learning, Video Processing, and Natural Language Processing

Amit Majithia

Faculty Status
Active
Title
Assistant Professor of Medicine and Pediatrics, Division of Endocrinology
Email
amajithia@ucsd.edu
Phone
(858) 822-0727
Track(s)
Bioinformatics and Systems Biology
Department
Brief Research Description
Insulin resistance, functional genomics, pharmacogenetics, cardiometabolic disease, diabetes
Lab Description

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.

Lukas Chavez

Faculty Status
Active
Title
Assistant Professor
Email
lukaschavez@ucsd.edu
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Department
Brief Research Description
Chromatin and Gene Regulation in Childhood Cancers
Lab Description

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.

Emma Farley

Faculty Status
Active
Title
Assistant Professor
Email
efarley@ucsd.edu
Phone
(858) 246-2559
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Department
Brief Research Description
High-throughput functional assays in developing embryos to decipher how genomes instruct development

Jejo Koola

Faculty Status
Active
Title
Assistant Clinical Professor
Email
jkoola@ucsd.edu
Phone
(858) 246-2563
Track(s)
Biomedical Informatics
Department
Brief Research Description
Practicing internist in the field of hospital medicine and clinical informatics. Research focus is on using informatics tools (including predictive analytics, natural language processing, and information visualization) to improve the care of multi-morbid hospital patients.
Lab Description

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.

Chun-Nan Hsu

Faculty Status
Inactive
Title
Associate Professor of Medicine
Email
chunnan@ucsd.edu
Phone
(858) 822-2690
Track(s)
Biomedical Informatics
Brief Research Description
Information extraction from biomedical literature and clinical notes by machine learning
Lab Description

My research focuses on solving natural language processing problems in biomedical sciences (bioNLP).

Sergei L. Kosakovsky Pond

Faculty Status
Inactive
Title
Assistant Adjunct Professor
Email
spond@ucsd.edu
Phone
(619) 543-8898
Track(s)
Biomedical Informatics
Department
BISB Research Area(s)
Brief Research Description
Computational biology, molecular evolution, phylogenetics and statistical genetics with an emphasis on HIV evolution