6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution, Fall 2008
Name
6-047-fall-2008/contents/index.htm
Size
34.45 KB
Format
HTML
Checksum (MD5)
2048c6b674b4f6deee8b88131f3459e7
Author(s)
Kellis, Manolis
Galagan, James
Alternative Title
Computational Biology: Genomes, Networks, Evolution
Date Issued
December 2008
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
Subjects
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
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Persistent DSpace Link
