This course on computational biology covers topics such as dynamic programming, database search, hidden Markov models, gene expression analysis, epigenomics, network analysis, deep learning, population genomics, genome assembly, phylogenetics, cancer genomics, and genome engineering. The course aims to teach students how to analyze genomes, networks, evolution, and health using computational tools. The teaching method includes lectures on various topics related to computational biology. This course is intended for individuals interested in genomics, bioinformatics, computational biology, and related fields.
MIT Computational Biology - Genomes, Networks, Evolution, Health - Fall 2018
Massachusetts Institute of Technology via YouTube
Overview
Syllabus
MIT CompBio Lecture 01 - Introduction.
MIT CompBio Lecture 02 - DynamicProgramming (Part1).
MIT CompBio Lecture 02 - DynamicProgramming (Part2).
MIT CompBio Lecture 03 - Database Search.
MIT CompBio Lecture 04 - HMMs I.
MIT CompBio Lecture 05 - HMMs II.
MIT CompBio Lecture 06 - Gene Expression Analysis: Clustering and Classification.
MIT CompBio Lecture 07 - RNA world, RNA-seq, RNA folding.
MIT CompBio Lecture 08 - Epigenomics.
MIT CompBio Lecture 09 - Three Dimensional Genome.
MIT CompBio Lecture 10 - Regulatory Genomics.
MIT CompBio Lecture 11 - Network Analysis.
MIT CompBio Lecture 12 - Deep Learning.
MIT CompBio Lecture 13 - Population Genomics.
MIT CompBio Lecture 14 - GWAS (part 1).
MIT CompBio Lecture 14 - GWAS (part 2).
MIT CompBio Lecture 15 - eQTLs.
MIT Compbio Lecture 16 - Heritability.
MIT CompBio Lecture 17 - Comparative Genomics.
MIT CompBio Lecture 18 - Genome Assembly, Evolution, Duplication.
MIT CompBio Lecture 19 - Phylogenetics.
MIT CompBio Lecture 20 - Phylogenomics.
MIT CompBio Lecture 21 - Single-Cell Genomics.
MIT CompBio Lecture 22 - Cancer Genomics.
MIT CompBio Lecture 23 - Multi-Phenotype analyses.
MIT CompBio Lecture 24 - Genome Engineering.
Taught by
Manolis Kellis
