6.874 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 7.01x, 18.600, and 6.041 as prerequisites.

6.874 will not be offered this semester. It will be available in the Spring semester, and will be instructed by D. K. Gifford.

This class counts for a total of 12 credits.

You can find more information on MIT OpenCourseWare at the Computational Functional Genomics site or on the 6.874 Stellar site.

MIT 6.874 Computational Systems Biology Related Textbooks
MIT 6.874 Computational Systems Biology On The Web
Computational Functional Genomics
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