Stability analysis of MTopGO for module identification in PPI networks

, ,
1 Laboratory of Informatics and Systems Engineering for Clinical Research, Clinical Scientific Institute Maugeri, Pavia, Italy
2 Department of Computer Science, Brunel University, London, United Kingdom
3 Department of Industrial Engineering and Information, University of Pavia, Pavia, Italy
DOI
10.7287/peerj.preprints.3289v1
Published
Accepted
Subject Areas
Bioinformatics, Algorithms and Analysis of Algorithms, Network Science and Online Social Networks
Keywords
Module detection, PPI Network, Stability Analysis, Modularity
Copyright
© 2017 Vella 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
Vella D, Tucker A, Bellazzi R. 2017. Stability analysis of MTopGO for module identification in PPI networks. PeerJ Preprints 5:e3289v1

Abstract

MTopGo is a novel algorithm of module identification for PPI Network analysis, it is designed to consider two key aspects of these models, the topological properties of the network and the apriori knowledge about the proteins involved, represented by GO annotations.

MTopGO rely on random components, thus stability of the results across different runs is a critical aspect of the algorithm. Moreover, when evaluating an algorithm specific for PPI Networks an important aspect is the stability in presence of false positive and false negative edges. In this work, two different stability analyses have been executed to evaluate MTopGO performance. Firstly, one to evaluate the stability of the result over many runs starting from a same input, to consider the range of variability introduced by the random components of the algorithm; secondly, one to evaluate the robustness of the output clusters when the input is affected by noise and uncertainty.

The results showed that MTopGO was more stable in case of false negative edges than false positive edges (adding false edges to a PPI Network was more damaging than removing existing links).

Author Comment

This is an abstract which has been accepted for the NETTAB 2017 Workshop