MIT Deep Learning Genomics - Lecture 6 - Regulatory Genomics (Spring 2020) скачать в хорошем качестве

MIT Deep Learning Genomics - Lecture 6 - Regulatory Genomics (Spring 2020)

MIT 6.874 Lecture 6. Spring 2020 Course website: https://mit6874.github.io/ Lecture slides: Lecturer: Manolis Kellis Lecture outline: 1. Biological foundations: Building blocks of Gene Regulation Gene regulation: Cell diversity, Epigenomics, Regulators (TFs), Motifs, Disease role Probing gene regulation: TFs/histones: ChIP-seq, Accessibility: DNase/ATAC-seq 2. Classical methods for Regulatory Genomics and Motif Discovery Enrichment-based motif discovery: Expectation Maximization, Gibbs Sampling Experimental: PBMs, SELEX. Comparative genomics: Evolutionary conservation. 3. Regulatory Genomics CNNs (Convolutional Neural Networks): Foundations Key idea: pixels == DNA letters. Patches/filters == Motifs. Higher == combinations Learning convolutional filters == Motif discovery. Applying them == Motif matches 4. Regulatory Genomics CNNs/RNNs in Practice: Diverse Architectures DeepBind: Learn motifs, use in (shallow) fully-connected layer, mutation impact DeepSea: Train model directly on mutational impact prediction Basset: Multi-task DNase prediction in 164 cell types, reuse/learn motifs ChromPuter: Multi-task prediction of different TFs, reuse partner motifs DeepLIFT: Model interpretation based on neuron activation properties DanQ: Recurrent Neural Network for sequential data analysis