6.047/6.878  Computational Biology: Genomes, Networks, Evolution

Covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

• Genomes:

  Biological sequence analysis, hidden Markov models, gene finding, comparative genomics, RNA structure, sequence alignment, hashing

• Networks:

  Gene expression, clustering/classification, EM/Gibbs sampling, motifs, Bayesian networks, microRNAs, regulatory genomics, epigenomics

• Evolution:

  Gene/species trees, phylogenomics, coalescent, personal genomics, population genomics, human ancestry, recent selection, disease mapping 

In addition to the technical material in the course, the term project provides practical experience: (1) writing an NIH-style research proposal, (2) reviewing peer proposals, (3) planning and carrying out independent research, (4) presenting research results orally in a conference setting, and (5) writing results in a journal-style scientific paper. You will work on a project of your choice with regular feedback and advice from a mentor, your peers, and the teaching staff.

How 6.047 relates to other Course VI classes and objectives: http://6004.mit.edu/gmap/ public.html?focus=6.047