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6.096 Algorithms for Computational Biology (MIT) 6.096 Algorithms for Computational Biology (MIT)

Description

This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks. This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

Subjects

biological sequence analysis | biological sequence analysis | gene finding | gene finding | motif discovery | motif discovery | RNA folding | RNA folding | global and local sequence alignment | global and local sequence alignment | genome assembly | genome assembly | comparative genomics | comparative genomics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | gene expression | gene expression | clustering algorithms | clustering algorithms | scale-free networks | scale-free networks | machine learning applications | machine learning applications

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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6.096 Algorithms for Computational Biology (MIT)

Description

This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

Subjects

biological sequence analysis | gene finding | motif discovery | RNA folding | global and local sequence alignment | genome assembly | comparative genomics | genome duplication | genome rearrangements | evolutionary theory | gene expression | clustering algorithms | scale-free networks | machine learning applications

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-allcourses.xml

Attribution

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

All metadata

See all metadata