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6.096 Algorithms for Computational Biology, Spring 2005

Challenges in Computational Biology

Pictographic representation of the challenges in computational biology. (Figure by MIT OCW. Courtesy of Prof. Manolis Kellis.)

Highlights of this Course

This course features a complete set of homework assignments. In addition, a partial list of lecture notes is available.

Course 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.

Technical Requirements

Use the Python interpreter to run the .py files found on this course site.



Prof. Manolis Kellis

Course Meeting Times

One session / week
1.5 hours / session



Additional Features

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