Prediction of interface residue based on features of residue interaction network extracted using shapley value centrality measure
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Science
- Keywords
- Ant Colony Optimization, Shapley Value, Residue Interaction Network, Naïve Bayes classifier, Classification, Naïve Baye’s classifier
- Copyright
- © 2018 Pandey et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Prediction of interface residue based on features of residue interaction network extracted using shapley value centrality measure. PeerJ Preprints 6:e26785v1 https://doi.org/10.7287/peerj.preprints.26785v1
Abstract
Protein-Protein interaction plays an important role in the life processes. Molecular mechanisms of the related processes can be better understood with the help of interface prediction. In this work, we use game theory concept of Shapley value to analyse the spatial relationship between residues in residue interaction network. Four features are extracted from network using shapley value and given as input to ACO for optimization. Our experiment shows that optimized feature set, significantly improves the result of normal classifier and accuracy from 80% to 85%. These findings are useful for identifying protein-like complex networks. The presented results suggest that the feature selection by Shapley value and optimization by ACO improves the classification of protein structure at great extent less computational complexity.
Author Comment
This is a submission to PeerJ for review.
