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Probabilistic models for biological sequences: selection and Maximum Likelihood estimation.

Svetlana Ekisheva1, Mark Borodovsky

  • 1School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA. sveta.ekisheva@bme.gatech.edu

International Journal of Bioinformatics Research and Applications
|December 1, 2007
PubMed
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This study introduces statistical tests for analyzing biological sequences like DNA and proteins. It helps select the best probabilistic models and provides confidence intervals for model parameters.

Area of Science:

  • Bioinformatics and computational biology
  • Statistical modeling of biological data

Background:

  • Probabilistic models are essential for analyzing biological sequences (DNA, proteins).
  • Accurate model selection and parameter estimation are crucial for biological sequence analysis.

Purpose of the Study:

  • To develop statistical tests for identifying dependencies in biological sequences.
  • To guide the selection of appropriate probabilistic models for experimental sequences.
  • To provide theoretical bounds for parameter estimation accuracy in selected models.

Main Methods:

  • Statistical hypothesis testing to detect sequence element dependencies.
  • Derivation of uniform lower bounds for error rates of Maximum Likelihood (ML) estimates.
  • Development of uniform confidence intervals for model parameters.

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Main Results:

  • The study presents methods to determine the order of dependency in biological sequences.
  • It offers a framework for choosing between independence models, first-order Markov chains, and hidden Markov models (HMMs).
  • Theoretical guarantees for the accuracy of parameter estimates are established.

Conclusions:

  • The developed statistical tests enhance the reliability of biological sequence modeling.
  • This work provides a rigorous approach to model selection and parameter estimation in bioinformatics.
  • The findings contribute to more accurate analysis of DNA and protein sequences using probabilistic methods.