MIT Subject Listing & Schedule
IAP / Spring 2019 Search Results

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6.047 Computational Biology: Genomes, Networks, Evolution

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(Subject meets with 6.878[J], HST.507[J])
Prereq: (Biology (GIR), 6.006, and 6.041B) or permission of instructor
Units: 3-0-9

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.
M. Kellis