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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A shortest-path graph kernel for estimating gene product semantic similarity.

Marco A Alvarez1, Xiaojun Qi, Changhui Yan

  • 1Department of Computer Science, North Dakota State University, Fargo, 58108, USA. changhui.yan@ndsu.edu.

Journal of Biomedical Semantics
|August 2, 2011
PubMed
Summary

A new shortest-path graph kernel (spgk) method calculates gene product semantic similarity using only the Gene Ontology (GO). This intrinsic approach avoids external resource bias and shows strong performance.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene Ontology (GO) semantic similarity calculations often depend on external resources, introducing potential bias.
  • Shifting research trends can alter external resource distributions, impacting similarity metric reliability.
  • Intrinsic methods, independent of external data, offer a more stable approach to GO semantic similarity.

Purpose of the Study:

  • To develop and evaluate a novel, intrinsic method for calculating semantic similarity between gene products using the Gene Ontology.
  • To address the limitations of existing methods that rely on external, potentially biased resources.
  • To provide a robust alternative for GO semantic similarity computation.

Main Methods:

  • A shortest-path graph kernel (spgk) method was developed, utilizing exclusively the Gene Ontology and its inherent structure.
  • Gene products are represented as induced subgraphs within the GO, encompassing all associated GO terms.
  • The spgk method employs a graph kernel to compute similarity between these gene product-specific subgraphs.

Main Results:

  • The spgk method demonstrated favorable performance compared to methods relying on external resources in benchmark evaluations.
  • spgk achieved slightly superior results over simUI, another intrinsic GO method, on a benchmark dataset.
  • Statistical analysis indicated significant improvements in performance when using resolution and EC similarity correlation coefficients, but not for the Pfam similarity correlation coefficient.

Conclusions:

  • The spgk method leverages a polynomial-time graph kernel to exploit GO structure for gene product semantic similarity.
  • spgk offers a viable alternative to both external-resource-dependent and existing intrinsic methods for GO semantic similarity.
  • The approach provides comparable performance while enhancing robustness by relying solely on the Gene Ontology.