A NOVEL SEMANTIC SIMILARITY SCORE FOR PROTEIN DATA ANALYSIS

Authors

  • Anooja Ali School of CSE, REVA University, Bengaluru
  • Vishwanath R Hulipalled School of C&IT, REVA University, Bengaluru
  • S.S. Patil Department of Agricultural Statistics, University of Agriculture Sciences, Bengaluru, Karnataka, India

DOI:

https://doi.org/10.58973/CTRJ.22111

Keywords:

Annotation, Information Content, Membership, Topology

Abstract

Aim: A similarity evaluation measure for Gene Ontology GO terms is developed.

Results: The proposed method takes into account the semantics hidden in ontologies or the term level information content, membership of term, and topology-based similarity measures. The proposed method is evaluated on positive and negative dataset of UniProt, Protein family clans and the Pearson’s correlation with other existing methods.

Conclusion: The experimental results exhibited a major supremacy of the proposed method over other semantic similarity measures.

HIGHLIGHTS:
1. An improved approach for semantic similarity evaluation for GO terms based on the information content and the topological factors is developed.
2. The proposed method shows highest correlation for MF (Molecular Function) ontology.

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Published

2022-10-05

How to Cite

Ali, A., R Hulipalled, V., & S.S. , P. (2022). A NOVEL SEMANTIC SIMILARITY SCORE FOR PROTEIN DATA ANALYSIS. Computing Technology Research Journal, 1(1), 1–4. https://doi.org/10.58973/CTRJ.22111

Issue

Vol. 1 No. 1 (2022): October: Computing Technology Research Journal

Section

Short Communication

License

Copyright (c) 2022 Computing Technology Research Journal

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