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| Course info
(This page refers to the fall 2005 version of the class now listed as BioE241. Please refer to the BioE241 page for up-to-date class info)
- Title: Probabilistic modeling in computational biology
- Instructor: IanHolmes
- Class: BioE 290B (course entry code 07702)
- When: Fall 2005
- Lectures: MW 12-1:30, 458 Evans Hall
Lecture notes
PDF format.
Recommended textbooks
Primary texts (probabilistic modeling and bioinformatics)
- Durbin, Eddy, Krogh and Mitchison. Biological Sequence Analysis. ISBN:0521629713
- MacKay. Information Theory, Inference and Learning Algorithms. ISBN:0521642981
- Can be downloaded from here (copyright grants permission to view but not print)
Sections of the following are also useful as background
For continuous Markov processes
Brief description of syllabus
Molecular grammars
The metaphor of DNA sequence as "the language of life" is so over-used as to
have become trite.
Yet, remarkably, there are deep mathematical parallels between DNA and natural language.
How do these similarities arise? How can they be measured and put to use,
particularly in the high-throughput mode that characterises modern genomics?
This class examines these questions from the point
of view of someone interested in developing probabilistic modeling algorithms
from evolutionary principles.
We will also examine several other kinds of probabilistic model,
useful both in bioinformatics and in other areas of applied machine learning.
Probabilistic modeling
Probabilistic methods - including graphical models, HMMs, Gaussian
processes, stochastic grammars, Markov Chain Monte Carlo, Markov random
fields, etc. - are a mainstay of modern computer science applications. They
are natural heirs to earlier fields of research such as expert systems,
artificial intelligence and neural networks.
One area in which probabilistic methods have made a particularly strong
impact in the past decade is computational biology. Studying probabilistic
algorithms in the context of molecular biology offers a uniquely interesting
background to these methods. Not only is the probabilistic analysis directly
transferable to other applications in CS and scientific informatics, but the
the application to post-genomic biology provides an entry point into such
breaking areas as synthetic biology, human genome evolution, molecular
ecology and gene circuit analysis.
This class will develop probabilistic modeling techniques,
particularly time-evolving random processes and stochastic
grammars. A strong emphasis on underlying theoretical techniques will be
complemented by reference to working code that can be applied to
real problems in phylogenetics and genomic analysis.
A central theme of the course is the increasingly popular use of evolutionary grammars as a foundation for genomics algorithms (see PhylogeneticAlignmentReader).
We will also cover other aspects of Stochastic Biology.
Assignments
Some ideas for future reading/presentation assignments:
ProteinCovariation, EvolutionOfFunction, StochasticGrammarApplications;
plus selected papers from the following lists: StochasticBiology, PhylogeneticAlignmentReader
See also Previous Graduate Class Assignments.
Announcements
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