6.895 / 6.095J Computational Biology: Genomes, Networks, Evolution, Fall 2005
Name
6-895Fall-2005/OcwWeb/Electrical-Engineering-and-Computer-Science/6-895Fall-2005/CourseHome/index.htm
Size
16.35 KB
Format
HTML
Checksum (MD5)
6113e10f5319832b08fe65d9a1e74250
Author(s)
Kellis, Manolis
Indyk, Piotr
Alternative Title
Computational Biology: Genomes, Networks, Evolution
Date Issued
December 2005
Abstract
This course is offered to both undergraduates and graduates. The undergraduate version of the course includes a midterm and final project. The graduate version of the course includes additional assignments and a more ambitious final project, which can lead to a thesis or publication. Focus will be 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
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Persistent DSpace Link
