Machine Learning for Genomics Fall 2020

Fall 2020 Prof. Manolis Kellis Computational Biology: Genomes, Networks, Evolution, Health Machine Learning in Genomics: Dissecting the circuitry of Human Disease

6.047/6.878 Lecture 1 - Introduction (Fall 2020).
6.047/6.878 Lecture 2 - Dynamic Programming (Fall 2020).
6.047/6.878 Lecture 3 - Local alignment Hashing BLAST alignmentScores (Fall 2020).
6.047/6.878 Lecture 4 - HMMs 1 (Fall 2020).
6.047/6.878 Lecture 5 - HMMs 2 (Fall 2020).
6.047/6.878 Lecture 6 - Expression analysis Clustering Classification (Fall 2020).
6.047/6.878 Lecture 7 - RNA folding, RNA world, RNA structures (Fall 2020).
6.047/6.878 Lecture 8 - Epigenomics 1 (Fall 2020).
6.047/6.878 Lecture 9 - Epigenomics 2 and 3D genome (Fall 2020).
6.047/6.878 Lecture 10 - Regulatory Genomics and Motifs (Fall 2020).
6.047/6.878 Lecture 11 - Networks (Fall 2020).
6.047/6.878 Lecture 12 - Deep Learning (Fall 2020).
6.047/6.878 Lecture 13 - Population Genetics (Fall 2020).
6.047/6.878 Lecture 14 - GWAS and Disease Dissection (Fall 2020).
6.047/6.878 Lecture 15 - eQTLs expression Quantitative Trait Loci (Fall 2020).
6.047/6.878 Lecture16 - Systems Genetics and Heritability (Fall 2020).
6.047/6.878 Lecture17 - Comparative Genomics (Fall 2020).
6.047/6.878 Lecture 18 - Genome Evolution (Fall 2020).
6.047/6.878 Lecture 19 - Phylogenetics (Fall 2020).
6.047/6.878 Lecture 20 - Phylogenomics (Fall 2020).
6.047/6.878 Lecture 21 - Cancer Genomics (Fall 2020).
MIT Deep Learning Genomics - Lecture 15 - Single-cell genomics (Spring 2020).