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Class info
- Title: Probabilistic Modeling, Genomics and Jurassic Park
- Instructor: IanHolmes
- Class: BioE 241
- When: Spring semester, 2009
- Lectures: Tue/Thurs 11-12:30, 7 Evans Hall
- Instructor blog -- please do post comments/questions.
In this class you will use statistics, DNA sequencing and/or paleontology to reconstruct the genomes of organisms that were ancestral to multiple present-day species.
Aspects of reconstruction that will be considered include inference algorithms; confidence estimates;
detail at multiple levels (DNA sequence, genes, pathways, systems);
theory and in silico experiments to investigate the accuracy of sequence reconstruction algorithms;
and experimental approaches to synthesis and empirical investigation of reconstructed sequence.
This page is currently under review.
Here is some older archived material: Bio E 241 Fall 07.
Syllabus
The syllabus for this class is loosely based on the syllabus for my Compbio Models Class whose theme is statistical inference in molecular evolution,
but some additional material pertaining to reconstruction theory & experiments will also be covered.
Paper review sessions
See the BioE241Presentations page.
Programming exercises
See the BioE241Projects page.
Lecture notes
Other materials
- That Stanford ribo-happening video
Recommended textbooks
Primary texts (probabilistic modeling and bioinformatics)
- The MacKay Book. MacKay. Information Theory, Inference and Learning Algorithms. ISBN:0521642981
- Can be downloaded from here (copyright grants permission to view but not print)
Background on molecular evolution & algorithms for doing it:
For continuous-valued Markov processes, Gaussian & otherwise:
- Rasmussen and Williams. Gaussian Processes for Machine Learning. ISBN:026218253X
Computational neuroscience:
- The Spikes Book. Rieke, Warland, de Ruyter van Steveninck and Bialek. Spikes: Exploring the Neural Code. ISBN:0262681080
- An excellent introduction to information theory & Bayesian analysis in computational neuroscience.
- Eliasmith and Anderson. Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems. ISBN:0262050714
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