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Ranked Gene Ontology Based Protein Function Prediction by Analysis of Protein–Protein Interactions

  • Kaustav Sengupta
  • Sovan Saha
  • Piyali Chatterjee
  • Mahantapas Kundu
  • Mita Nasipuri
  • Subhadip Basu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

Computational function prediction of unknown protein is a challenging task in proteomics. As protein–protein interactions directly contribute to the protein function, recent efforts attempt to infer about proteins’ functional group by studying their interactions. Recently, use of hierarchical relationship between functional groups characterized by Gene Ontology improves prediction ability compared to hierarchy unaware “flat” prediction methods. As a protein may have multiple functional groups with different degrees of evidences, function prediction is viewed as a complex multi-class classification problem. In this paper, we propose a method which assigns multiple Gene Ontology terms to unknown protein from its neighborhood topology using a ranking methodology showing different levels of association. This work achieves precision of 0.74, recall of 0.67, and F-score of 0.73, respectively, on 19,247 human proteins having 8,548,002 interactions in between themselves.

Keywords

Gene ontology (GO) Enrichment score Edge weight Shore protein Bridge protein Fjord protein Gene ontology similarity Protein–Protein interaction network (PPIN) 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Kaustav Sengupta
    • 1
  • Sovan Saha
    • 2
  • Piyali Chatterjee
    • 3
  • Mahantapas Kundu
    • 1
  • Mita Nasipuri
    • 1
  • Subhadip Basu
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Computer Science and EngineeringDr. Sudhir Chandra Sur Degree Engineering CollegeDum Dum, KolkataIndia
  3. 3.Department of Computer Science and EngineeringNetaji Subhash Engineering CollegeGaria, KolkataIndia

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