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HST 506
Computational Systems Biology

Term: Spring

Course Director(s): David K. Gifford
Time:
Lecture: T/Th 1-2:30pm; Recitation: W or Th or F 4pm
Location:
Lecture: MIT: 32-141; Recitation: MIT: W 66-156; Th or F MIT: 66-160
Course Website:
None
Prerequisite:
Biology (GIR) and (6.041B or 18.600)
Restrictions:
None
MIT Units:
3-0-9 (G-Level Credit)
Harvard Units:
Unknown
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.