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Hidden Markov Models, grammars, and biology: a tutorial.

Shibaji Mukherjee1, Sushmita Mitra

  • 1Association for Studies in Computational Biology, Kolkata 700 018, India. mshibaji@acm.org

Journal of Bioinformatics and Computational Biology
|April 27, 2005
PubMed
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This study explores Hidden Markov Models and functional grammars for modeling biological sequences and structures. It details mathematical concepts, algorithms, and applications for proteins and nucleic acids.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Biological sequence and structure modeling is crucial for understanding biomolecular function.
  • Various machine learning techniques and mathematical concepts have been applied.
  • Hidden Markov Models and functional grammars are prominent methods.

Purpose of the Study:

  • To survey and formally introduce Hidden Markov Models and functional grammars for biological data analysis.
  • To provide a comprehensive mathematical description of these modeling techniques.
  • To discuss their application in analyzing biological sequences and modeling biomolecular structures.

Main Methods:

  • Formal introduction to Hidden Markov Models (HMMs) and grammars.
  • Detailed mathematical descriptions of algorithms.

Related Experiment Videos

  • Application of HMMs and grammars to protein and nucleic acid analysis.

    • Main Results:

    • Discussion of basic algorithms for sequence analysis.
    • Modeling of protein and nucleic acid structures using HMMs and grammars.
    • Comparison of different modeling approaches.

    Conclusions:

    • Highlighting potential research areas and challenges in biological data modeling.
    • Mentioning available databases and software for HMMs and grammars.
    • Emphasizing the natural continuity and mathematical rigor of the discussed methods.