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This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice.
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English
English [CC]
FREE
- Learn basic syntax that can apply to any language.
- Learn what is a programming language and the basic concepts for beginners.
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Description
We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets
Course content
- Introduction: Course overview, biology, algorithms, machine learning Unlimited
- Alignment I: Dynamic programming, global and local alignmen Unlimited
- Alignment II: Database search, rapid string matching, BLAST, BLOSUM Unlimited
- Hidden Markov Models Part 1: Evaluation/parsing, Viterbi, forward algorithms Unlimited
- Hidden Markov Models Part 2: Posterior decoding, learning, Baum-Welch Unlimited
- Expression Analysis: Clustering/classification, k-means, hierarchical, Bayesian Unlimited
- Networks II: Network learning, structure, spectral methods Unlimited
- Regulatory Motifs: Discovery, representation, PBMs, Gibbs sampling, EM Unlimited
- Epigenomics: ChIP-seq, read mapping, peak calling, IDR, chromatin states Unlimited
- Comparative genomics and evolutionary signatures Unlimited
- Phylogenetics: Molecular evolution, tree building, phylogenetic inference Unlimited
- Personal genomics, disease epigenomics: Systems approaches to disease Unlimited
Instructor
Massachusetts Institute of Technology
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