Markov and Hidden Markov Models of Genomic and Protein Features 
Markov and Hidden Markov Models of Genomic and Protein Features by MIT
Video Lecture 10 of 22
Copyright Information: Christopher Burge, David Gifford, and Ernest Fraenkel. 7.91J Foundations of Computational and Systems Biology, Spring 2014. (Massachusetts Institute of Technology: MIT OpenCourseWare), (Accessed 16 Feb, 2015). License: Creative Commons BY-NC-SA
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Date Added: February 16, 2015

Lecture Description

Instructor: Christopher Burge

Prof. Christopher Burge begins by reviewing Lecture 9, then begins his lecture on hidden Markov models (HMM) of genomic and protein features. He addresses the terminology and applications of HMMs, the Viterbi algorithm, and then gives a few examples.

Course Index

Course Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. This course is designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science, but not necessarily both. The scripting language Python—which is widely used for bioinformatics and computational biology—will be used; foundational material covering basic programming skills will be provided by the teaching assistants. Graduate versions of the course involve an additional project component.

Instructors: Christopher Burge, David Gifford, Ernest Fraenkel


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