Please visit the following page for an updated listing of graduate courses in Bioinformatics at UCLA:
http://bioinformatics.ucla.edu/graduate-courses/
First Year Course work The Bioinformatics core curriculum is designed to provide students with a foundation for performing research in the field of bioinformatics. The courses are designed to teach the shared concepts, language and skills that bioinformaticists must have to operate in a collaborative, inter-disciplinary scientific environment. The topics covered include statistical inference, computational complexity, network analysis and data mining. The core curriculum provides a common vocabulary and set of concepts that are considered essential. Once students have completed the first year curriculum detailed below, they have the flexibility to substantiate their training with by selecting courses from a large number of Electives to tailor to their individual research interests and background.
Fall Quarter
- BIOINFO M260A. Introduction to Bioinformatics . (Same as Chemistry CM260A, Computer Science CM221, and Human Genetics M260A.) Introduction to bioinformatics and methodologies, with emphasis on concepts and inventing new bioinformatic methods. Focus on sequence analysis and alignment algorithms.
- BIOINFO M202. Bioinformatics Interdisciplinary Research Seminar (Same as Chemistry M202.) 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.
- BIOINFO M252. Seminar: Advanced Methods in Computational Biology (Same as Chemistry M252 and Human Genetics M252.) Designed for graduate students. Examination of computational methodology in bioinformatics and computational biology through presentation of current research literature. How to select and apply methods from computational and mathematical disciplines to problems in bioinformatics and computational biology; development of novel methodologies.
Winter Quarter
- BIOINFO M252. Seminar: Advanced Methods in Computational Biology (Same as Chemistry M252 and Human Genetics M252.) Designed for graduate students. Examination of computational methodology in bioinformatics and computational biology through presentation of current research literature. How to select and apply methods from computational and mathematical disciplines to problems in bioinformatics and computational biology; development of novel methodologies.
***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.
Spring Quarter
- BIOINFO 224. Computational Genetics (Same as Human Genetics or Com Sci CM224..) Introduction to current quantitative understanding of human genetics and computational interdisciplinary research in genetics. Topics include introduction to genetics, human population history, linkage analysis, association analysis, association study design, isolated and admixed populations, population substructure, human structural variation, model organisms, and genotyping technologies. Computational techniques include those 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.
- BIOINFO M252. Seminar: Advanced Methods in Computational Biology (Same as Chemistry M252 and Human Genetics M252.) Designed for graduate students. Examination of computational methodology in bioinformatics and computational biology through presentation of current research literature. How to select and apply methods from computational and mathematical disciplines to problems in bioinformatics and computational biology; development of novel methodologies.