| Summary: | 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.
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