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

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dc.contributor.author Kellis, Manolis en_US
dc.contributor.author Galagan, James en_US
dc.coverage.temporal Fall 2008 en_US
dc.date.issued 2008-12
dc.identifier 6.047-Fall2008
dc.identifier local: 6.047
dc.identifier local: 6.878
dc.identifier local: IMSCP-MD5-ce3a7d8d7658d09e9a6be6fb1995f0bb
dc.identifier.uri http://hdl.handle.net/1721.1/103560
dc.description.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 en_US
dc.language en-US en_US
dc.relation en_US
dc.rights.uri Usage Restrictions: This site (c) Massachusetts Institute of Technology 2016. Content within individual courses is (c) by the individual authors unless otherwise noted. The Massachusetts Institute of Technology is providing this Work (as defined below) under the terms of this Creative Commons public license ("CCPL" or "license") unless otherwise noted. The Work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. By exercising any of the rights to the Work provided here, You (as defined below) accept and agree to be bound by the terms of this license. The Licensor, the Massachusetts Institute of Technology, grants You the rights contained here in consideration of Your acceptance of such terms and conditions. en_US
dc.rights.uri Usage Restrictions: Attribution-NonCommercial-ShareAlike 3.0 Unported en_US
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/ en_US
dc.subject computational biology en_US
dc.subject algorithms en_US
dc.subject machine learning en_US
dc.subject biology en_US
dc.subject biological datasets en_US
dc.subject genomics en_US
dc.subject proteomics en_US
dc.subject genomes en_US
dc.subject sequence analysis en_US
dc.subject sequence alignment en_US
dc.subject genome assembly en_US
dc.subject network motifs en_US
dc.subject network evolution en_US
dc.subject graph algorithms en_US
dc.subject phylogenetics en_US
dc.subject comparative genomics en_US
dc.subject python en_US
dc.subject probability en_US
dc.subject statistics en_US
dc.subject entropy en_US
dc.subject information en_US
dc.title 6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution, Fall 2008 en_US
dc.title.alternative Computational Biology: Genomes, Networks, Evolution en_US


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