MIT6_047f08_lec01_slide01

MIT6_047f08_lec01_slide01 - MIT OpenCourseWare...

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MIT OpenCourseWare http://ocw.mit.edu 6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms .
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6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution Manolis Kellis James Galagan
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Goals for the term • Introduction to computational biology – Fundamental problems in computational biology – Algorithmic/machine learning techniques for data analysis – Research directions for active participation in the field • Ability to tackle research – Problem set questions: algorithmic rigorous thinking – Programming assignments: hands-on experience w/ real datasets • Final project: – Research initiative to propose an innovative project – Ability to carry out project’s goals, produce deliverables – Write-up goals, approach, and findings in conference format – Present your project to your peers in conference setting
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Course outline • Organization – Duality: Computation and Biology • Important biological problems • Fundamental computational techniques – Foundations and Frontiers • First half: well-defined problems and general methodologies • Second half: in-depth look at complex problems, combine techniques learned, opens to projects, research directions • Topics covered – First half: the foundations • String matching, genome analysis, expression clustering/classification, regulatory motifs, biological networks, evolutionary theory, populations – Second half: the frontiers • Comparative genomics, Bayesian networks, systems biology, genome assembly, metabolic modeling, miRNA, genome evolution
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Why Computational Biology ?
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Why Computational Biology: Last year’s answers • Lots of data (* lots of data) • There are rules • Pattern finding • It’s all about data • Ability to visualize • Simulations • Guess + verify (generate hypotheses for testing) • Propose mechanisms / theory to explain observations • Networks / combinations of variables • Efficiency (reduce experimental space to cover) • Informatics infrastructure (ability to combine datasets) • Correlations • Life itself is digital. Understand cellular instruction set
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MIT6_047f08_lec01_slide01 - MIT OpenCourseWare...

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