6.047 Computational Biology: Genomes, Networks, Evolution

Class Info

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.

This class has 6.006, 6.041B, and 7.01x as prerequisites.

6.047 will be offered this semester (Fall 2017). It is 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.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.047&btnG=Google+Search&inurl=https site or on the 6.047 Stellar site.

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