The core curriculum of the Biomedical Informatics Track encompasses 2 years of study, providing an introduction to both clinical informatics (medical information architectures, study design and evaluation, decision-making theory, medical knowledge representation) and bioinformatics (computational genetics/genomics, bioinformatics algorithms and tools). Additionally, this core curriculum emphasizes translational research and rising opportunities in big data science. The first year is detailed below. The second year will consist of a curriculum tailored to the student’s interests comprising courses chosen from a large number of Electives.
- BIOINFOR M202. Bioinformatics Interdisciplinary Research Seminar. Concrete examples of how biological questions about genomics data map to and are solved by methodologies from other disciplines, including statistics, computer science, and mathematics.
- BIOINFOR M260A. Introduction to Bioinformatics. Introduction to bioinformatics and methodologies, with emphasis on concepts and inventing new computational and statistical techniques to analyze biological data. Focus on sequence analysis and alignment algorithms.
- BIOENG 227 Medical Information Architectures & Internet Technologies. Introduction to Biomedical Informatics, part I.
- BIOENG M228. Medical Decision-Making and Evaluation. Introduction to Biomedical Informatics, part II.
***NOTE: Students are required to complete 12 units of elective coursework, of which one can be a 100-level undergraduate course. Students may opt to enroll in an elective during this quarter. Electives must be approved by the IDP Faculty Graduate Advisor.
- BIOINFO M224. Computational Genetics. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics. Topics include introduction to genetics, identification of genes involved in disease, inferring human population history, technologies for obtaining genetic information, and genetic sequencing. Focus on formulating interdisciplinary problems as computational problems and then solving those problems using computational techniques from statistics and computer science.
- BIOINFO M271. Statistical Methods in Computational Biology. Introduction to statistical methods developed and widely applied in several branches of computational biology, such as gene expression, sequence alignment, motif discovery, comparative genomics, and biological networks, with emphasis on understanding of basic statistical concepts and use of statistical inference to solve biological problems.
- BIOENG M226. Medical Knowledge Representation. Introduction to Biomedical Informatics, part III.