Hidden Markov models and other machine learning approaches in computational molecular biology
- California Inst. of Tech., Pasadena, CA (United States)
This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In this tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.
- Research Organization:
- Stanford Univ., CA (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG03-95ER62031
- OSTI ID:
- 373868
- Report Number(s):
- CONF-9507246-6; ON: DE96014301; TRN: AHC29619%%51
- Resource Relation:
- Conference: Intelligent Systems for Molecular Biology (ISMB) conference, Cambridge (United Kingdom), 16-19 Jul 1995; Other Information: PBD: [1995]
- Country of Publication:
- United States
- Language:
- English
Similar Records
Molecular biology for the computer scientist
Intelligent systems for the molecular biologist