6.047 Computational Biology: Genomes, Networks, Evolution
Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks.
6.047 will not be offered this semester. It will be available in the Fall semester, and will be instructed by M. Kellis.
Lecture occurs 1:00 PM to 2:30 PM on Tuesdays and Thursdays in 32-141.
This class counts for a total of 12 credits.
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