Vol. 1 No. 1 (2023): Jan/June - Issue - 01
Articles

Bi-Cluster Based Analysis on Gene Ontology

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  • Meenakshi Sundaram A,
  • Anooja Ali,
  • S S Patil,
  • Ajil A
Meenakshi Sundaram A
School of Computer Science and Engineering, REVA University, Bangalore, Karnataka, India.
Anooja Ali
School of Computer Science and Engineering, REVA University, Bangalore, Karnataka, India.
S S Patil
Department of Agricultural Statistics, University of Agriculture Sciences, Bengaluru, India
Ajil A
School of Computer Science and Engineering, REVA University, Bangalore, Karnataka, India.
DOI: https://doi.org/10.5281/zenodo.8073114

Published 2023-06-23

Keywords

  • Bicluster Centrality,
  • Gene Ontology,
  • Mutual Information,
  • medical

How to Cite

Meenakshi Sundaram A, Anooja Ali, S S Patil, & Ajil A. (2023). Bi-Cluster Based Analysis on Gene Ontology. Milestone Transactions on Medical Technometrics, 1(1), 10–17. https://doi.org/10.5281/zenodo.8073114

Abstract

Understanding biological activity requires the detection of crucial proteins. The identification of significant genes throughout the entire genome is advantageous for a number of reasons, including the categorization of critical genes for health and sickness, the rational creation of drugs, etc. Statistical methods have been suggested for predicting essential or requisite proteins/gene/GO terms, employed in protein networks.  The computational approaches focusing on the topological characteristics or centrality approaches ignore the biologically relevant intrinsic features of essential proteins. Hence, considering the biological aspects like expression data, subcellular information, annotation data, and orthologous relationships can improve accuracy. So, in this research, bi-clustering algorithm is used to detect the essential Gene Ontology (GO) terms in molecular, cellular and biological processes by evaluating the protein associations and encoding the associations with ontology terms and pathways. The proposed method encodes each protein in terms of Mutual Information (MI) score, GO annotation and vector-based GO encoded matrix is generated and the essential proteins are extracted. The validation of the proposed method is verified using different statistical measures on the datasets.
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