Course Descriptions

BIOMEDIN 202 Introductory Biomedical Informatics. This is a survey of fundamental and current topics in biomedical informatics and is meant to give an overview of current research problems in biomedical informatics and computational approaches that address those problems. Topics include medical security and privacy, electronic medical records, controlled terminologies and biomedical ontologies, electronic retrieval, technology-assisted learning environments, medical decision-making and decision support, sequence analysis, phylogenetics, biological networks and pathways, microarray analysis, natural language processing, and protein structural analysis and prediction. Enrollment limited to medical students or with the permission of the instructor (3 units; on line).

BIOMEDIN 210 Introduction - Fundamental Methods. Introduction to Biomedical Informatics: Fundamental Methods (Same as Computer Science 270) - Issues in the modeling, design, and implementation of computational systems for use in biomedicine. Topics: basic knowledge representation, controlled terminologies in medicine and biological science, fundamental algorithms, information dissemination and retrieval, knowledge acquisition, and ontologies. Emphasis is on the principles of modeling data and knowledge in biomedicine and on translation of resulting models into useful automated systems (3 units).

BIOMEDIN 215 Data Driven Medicine - With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: CS 106A; familiarity with statistics (STATS 202) and biology. Recommended: one of CS 246 (previously CS 345A), STATS 305, or CS 229 (3 units).

BIOMEDIN 260 Computational Methods for Biomedical Image Analysis and Interpretation (Same as Rad 260). Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science (2 or 4 units).