MIT6_047f08_lec01_note01 - MIT OpenCourseWare...

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MIT OpenCourseWare 6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution Fall 2008 For information about citing these materials or our Terms of Use, visit: .
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Lecture 1 Aims: An introduction to the course, and an introduction to molecular biology Administrative Details: -There were 4 handouts in class: a course information handout, the first problem set (due on Sept. 15 at 8 pm), the scribe policy, and a course survey. All of these are on the web page. -The first precept was on Friday 9/5 at 11: pm. The precept notes are posted on the web page. -There are 3 textbooks; they are on reserve at both MIT and at BU libraries. The course uses 3 books because no single book covers the all of the material in the class. No individual book covers both the algorithmic and the machine learning topics that will be addressed in the class. Also, because computational biology is a rapidly changing field, books quickly become outdated. The books are: (1) Biological sequence analysis (Durbin, Eddy, Krogh, and Mitchison). The caveat regarding this book: it is about 10 years old, which is a long time in computational biology. (2) An introduction to bioinformatics algorithms (Jones & Pevzner). As the title suggests, this book is a good resource for learning about bioinformatics algorithms. (3) Pattern classification (Duda, Hart, and Stork). A machine learning book. Why Computational Biology? There are a number of reasons that it is appropriate and useful to apply computational approaches to the study of biological data. -Many aspects of biology (such as sequence information) are fundamentally digital in nature. This means that they are well suited to computational modeling and analysis. -New technologies (such as sequencing, and high-throughput experimental techniques like microarray, yeast two-hybrid, and ChIP-chip assays) are creating enormous and increasing amounts of data that can be analyzed and processed using computational techniques -Running time & memory considerations are critical when dealing with huge datasets . An algorithm that works well on a small genome (for example, a bacteria) might be too time or space inefficient to be applied to 1000 mammalian genomes. Also, combinatorial questions dramatically increase algorithmic complexity. -Biological datasets can be noisy, and filtering signal from noise is a computational problem. -Machine learning approaches are useful to make inferences, classify biological features, & identify robust signals. -It is possible to use computational approaches to find correlations in an unbiased way, and to come up with conclusions that transform biological knowledge. This approach is called data-driven discovery.
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This note was uploaded on 09/24/2010 for the course EECS 6.047 / 6. taught by Professor Manoliskellis during the Fall '08 term at MIT.

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MIT6_047f08_lec01_note01 - MIT OpenCourseWare...

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