HST.506[J] Computational Systems Biology
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.
HST.506[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.
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