Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Optimally parsing a sequence into different classes based on multiple types of evidence

G D Stormo1, D Haussler

  • 1Dept. of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder 80309-0347, USA.

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1994
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Influence of MP 29-02 on ciliary beat frequency in human epithelial cells in vitro.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2018
Same author

Integrating genomes.

Science (New York, N.Y.)·2012
Same author

The UCSC Genome Browser Database: update 2009.

Nucleic acids research·2008
Same author

The UCSC Genome Browser Database: 2008 update.

Nucleic acids research·2007
Same author

The UCSC genome browser database: update 2007.

Nucleic acids research·2006
Same author

The UCSC Genome Browser Database: update 2006.

Nucleic acids research·2005

This study introduces a dynamic programming method for parsing biological sequences, combining multiple evidence types for improved accuracy in genomic and protein analysis. The algorithm efficiently finds optimal and sub-optimal parses and their probabilities.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Sequence Analysis

Background:

  • Biological sequences (genomic DNA, proteins) require parsing into functional subsequences like exons, introns, or secondary structure domains.
  • Classification of these subsequences often relies on multiple, imperfect evidence sources, necessitating integrated analysis for improved accuracy.

Purpose of the Study:

  • To develop a robust computational framework for parsing biological sequences using a weighted combination of diverse evidence.
  • To enhance the accuracy of subsequence classification in genomic and protein data through a unified approach.

Main Methods:

  • A dynamic programming algorithm is employed to determine optimal and sub-optimal parses for sequences based on weighted evidence.
  • The algorithm calculates the probability of parses under a Boltzmann-Gibbs distribution, enabling probabilistic assessment of classifications.

Related Experiment Videos

  • Gradient descent is utilized to optimize evidence weights by maximizing the probability of known correct parses in training datasets.

    • Main Results:

    • The dynamic programming approach successfully identifies optimal and sub-optimal parses for sequences with given evidence weightings.
    • Probabilistic evaluation of parses is achieved, providing a measure of confidence for the identified subsequences.
    • A method for learning optimal evidence weights from labeled data is established, improving predictive performance.

    Conclusions:

    • The developed dynamic programming algorithm provides an efficient and accurate method for parsing biological sequences.
    • Combining multiple evidence types through weighted averaging significantly improves classification accuracy compared to single-evidence methods.
    • The framework facilitates the optimization of evidence weighting, leading to more reliable predictions in bioinformatics applications.