6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution, Fall 2008

Author(s)
Kellis, Manolis; Galagan, James
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Alternative title
Computational Biology: Genomes, Networks, Evolution
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Abstract
This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution
Date issued
2008-12
URI
http://hdl.handle.net/1721.1/103560
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Other identifiers
6.047-Fall2008
local: 6.047
local: 6.878
local: IMSCP-MD5-ce3a7d8d7658d09e9a6be6fb1995f0bb
Keywords
computational biology, algorithms, machine learning, biology, biological datasets, genomics, proteomics, genomes, sequence analysis, sequence alignment, genome assembly, network motifs, network evolution, graph algorithms, phylogenetics, comparative genomics, python, probability, statistics, entropy, information
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