Gene Expression Prediction - Lecture 09 - Deep Learning in Life Sciences (Spring 2021)

6.874/6.802/20.390/20.490/HST.506 Spring 2021 Prof. Manolis Kellis Deep Learning in the Life Sciences / Computational Systems Biology Playlist:    • MIT Deep Learning in Life Sciences - Sprin...   Latest slides and course today: http://compbio.mit.edu/6874 Spring 2021 slides and materials: http://mit6874.github.io/ 0:00 Introduction 1:25 Obtaining gene expression data 6:05 Analyzing gene expression data 12:16 Up-sampling 13:20 Compressive sensing 16:30 Predicting splicing 17:40 Enchancer discovery 19:05 STARR-seq 22:00 Enchancer detection with weakly supervised learning 26:45 Model performance 31:15 Deep learning in gene expression analysis 43:18 Connectivity map project 48:02 Predicting gene expression from landmark genes 50:40 Deep generative models for genomics 52:07 Autoencoders 53:40 Generative models and variational autoencoders 1:02:14 Conditional VAE 1:05:20 Predicting splicing from primary sequence 1:08:20 RNA splicing 1:09:21 Decoding splicing with deep learning 1:10:51 Model performance, interpretation, and application