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Related Concept Videos

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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Published on: July 25, 2013

Nonparametric combinatorial sequence models.

Fabian L Wauthier1, Michael I Jordan, Nebojsa Jojic

  • 1Computer Science Division, University of California, Berkeley, California, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 4, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nonparametric prior for analyzing biological sequences with complex combinatorial structures. Our method effectively models these structures, outperforming simpler models and leading predictors in MHC binding prediction.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Biological sequences often contain linked positions that covary, forming complex combinatorial structures.
  • Existing methodologies for analyzing such sequences are still under development.

Purpose of the Study:

  • To develop a novel nonparametric prior for modeling biological sequences with emergent combinatorial structures.
  • To create a method that induces a posterior distribution over factorized sequence representations.

Main Methods:

  • Developed a nonparametric prior that allows combinatorial structures to emerge in sequence data.
  • Applied the prior to induce a posterior distribution over factorized sequence representations.
  • Conducted experiments on three biological sequence families.

Main Results:

  • Confirmed the presence of combinatorial structures in biological sequences.
  • Demonstrated that combinatorial sequence models provide more succinct descriptions than mixture models.
  • Showcased the utility of the posterior distribution in MHC binding prediction.

Conclusions:

  • The proposed nonparametric prior effectively models complex combinatorial structures in biological sequences.
  • The method offers a more succinct and accurate representation compared to existing models.
  • Integrating out the posterior yields favorable performance in MHC binding prediction tasks.