6.047/6.878  Computational Biology: Genomes, Networks, Evolution

Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study principles of algorithm design, influential problems and techniques, and analyze large-scale biological datasets.

• Genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search;

• Networks: gene expression analysis, regulatory motifs, biological network analysis;

• 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.

Units: 3-0-9
Prerequisites: 6.001, 7.01, 6.041

• http://stellar.mit.edu/S/course/6/fa05/6.095/
• http://stellar.mit.edu/S/course/6/fa06/6.895/

• https://hkn.mit.edu/6guide/src/fall05/6095.html
• https://sixweb.mit.edu/search/show_eval/6.085-f2006
• https://sixweb.mit.edu/search/show_eval/6.895-f2006