Foundations of statistical methods for multiple sequence alignment and structure prediction
- New York State Dept. of Health, Albany, NY (United States). Wadsworth Center for Labs. and Research
Statistical algorithms have proven to be useful in computational molecular biology. Many statistical problems are most easily addressed by pretending that critical missing data are available. For some problems statistical inference in facilitated by creating a set of latent variables, none of whose variables are observed. A key observation is that conditional probabilities for the values of the missing data can be inferred by application of Bayes theorem to the observed data. The statistical framework described in this paper employs Boltzmann like models, permutated data likelihood, EM, and Gibbs sampler algorithms. This tutorial reviews the common statistical framework behind all of these algorithms largely in tabular or graphical terms, illustrates its application, and describes the biological underpinnings of the models used.
- Research Organization:
- Stanford Univ., CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research, Washington, DC (United States)
- DOE Contract Number:
- FG03-95ER62031
- OSTI ID:
- 414035
- Report Number(s):
- CONF-9507246-8; ON: DE96014305
- 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
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