6.874[J] Computational Systems Biology


Class Info

Presents advanced machine learning and algorithmic approaches for contemporary problems in biology drawing upon recent advances in the literature. Topics include biological discovery in heterogeneous cellular populations; single cell data analysis; regulatory factor binding; motif discovery; gene expression analysis; regulatory networks (discovery, validation, data integration, protein-protein interactions, signaling, chromatin accessibility analysis); predicting phenotype from genotype; and experimental design (model validation, interpretation of interventions). Computational methods presented include deep learning, dimensionality reduction, clustering, directed and undirected graphical models, significance testing, Dirichlet processes, and topic models. Multidisciplinary team-oriented final research project.

This class has 18.600, and 6.041B as prerequisites.

6.874[J] will not be offered this semester. It will be instructed by D. K. Gifford.

This class counts for a total of 12 credits. This is a graduate-level class.

You can find more information at the Deep Learning: Theory, Algorithms and Applications site or on the 6.874[J] Stellar site.

MIT 6.874[J] Computational Systems Biology Related Textbooks
MIT 6.874[J] Computational Systems Biology On The Web
Deep Learning: Theory, Algorithms and Applications
Tags
recognition invariance representation learning workshop intuitive physics computational principles of macaque face processing system computational mcgovern institute for brain research tomaso poggio

© Copyright 2015