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6.895 Computational Biology: Genomes, Networks, Evolution (MIT) 6.895 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

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. 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.

Subjects

Genomes: Biological sequence analysis | Genomes: Biological sequence analysis | hidden Markov models | hidden Markov models | gene finding | gene finding | RNA folding | RNA folding | sequence alignment | sequence alignment | genome assembly | genome assembly | Networks: Gene expression analysis | Networks: Gene expression analysis | regulatory motifs | regulatory motifs | graph algorithms | graph algorithms | scale-free networks | scale-free networks | network motifs | network motifs | network evolution | network evolution | Evolution: Comparative genomics | Evolution: Comparative genomics | phylogenetics | phylogenetics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | rapid evolution | rapid evolution

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.895 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

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.

Subjects

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

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

Site sourced from

https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata