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Inferring gene ontologies from pairwise similarity data.

Michael Kramer1, Janusz Dutkowski1, Michael Yu1

  • 1Department of Medicine and Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Bioinformatics (Oxford, England)
|June 17, 2014
PubMed
Summary

This study introduces Clique Extracted Ontology (CliXO), an algorithm for inferring gene ontologies from omics data. CliXO accurately reconstructs biological process ontologies, outperforming other methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • The Gene Ontology (GO) is crucial but manually curated.
  • Inferring GO directly from omics data presents a significant challenge.
  • Existing methods struggle to capture hierarchical structure and biological pleiotropy.

Purpose of the Study:

  • To evaluate algorithms for inferring gene ontologies from omics data.
  • To assess the ability of Clique Extracted Ontology (CliXO) and LocalFitness to reconstruct GO.
  • To compare these methods against standard clustering techniques.

Main Methods:

  • Considered Clique Extracted Ontology (CliXO) and LocalFitness algorithms.
  • CliXO identifies maximal cliques in similarity networks.
  • Evaluated reconstruction of GO biological process ontology using semantic similarity and yeast omics datasets.

Main Results:

  • CliXO achieved >99% precision and recall reconstructing GO from semantic similarity.
  • Using omics data, CliXO outperformed other methods, reaching ~30% precision and recall.
  • CliXO demonstrated superior performance over LocalFitness and standard clustering on omics datasets.

Conclusions:

  • Developed an algorithmic foundation for gene ontology inference from biomolecular data.
  • CliXO effectively captures hierarchical and pleiotropic structures.
  • This work advances automated GO construction from omics data.