Manolis Kellis
@manoliskellis1·24.9K subscribers·255 videos
MIT CompBio Lecture 01 - Introduction
Posted
October 3, 2018
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53,044
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October 3, 2018
53,044
780
18
1.50%
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1:22:11Now PlayingMIT Computational Biology: Genomes, Networks, Evolution, Health
Prof. Manolis Kellis
Fall 2018
Covers the computational foundations and research frontiers of computational biology. Advanced algorithmic techniques for rapid genome analysis and interpretation, data integration, epigenomics, comparative genomics, regulatory genomics, single-cell biology, deep learning, bayesian networks, pattern finding, and dissecting diseaes mechanisms.
Genomes: Biological sequence analysis, hidden Markov models, gene finding, comparative genomics, RNA structure, sequence alignment, hashing.
Networks: Gene expression, clustering/classification, EM/Gibbs sampling, motifs, Bayesian networks, Deep Learning, Epigenomics, Single-cell Genomics.
Evolution: Gene/species trees, phylogenomics, coalescent, personal genomics, population genomics, human ancestry, recent selection, disease mapping.
Health: Genetic association mapping, common/rare variants, GWAS, PheWAS, multi-trait mapping, causality/mediation, EHR mining, cancer genomics, CRISPR.
In addition to the technical material in the course, the term project provides practical experience (1) writing an NIH-style research proposal, (2) reviewing peer proposals, (3) planning and carrying out independent research, (4) presenting research results orally in a conference setting, and (5) writing results in a journal-style scientific paper.
Slides for Lecture 1:
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