Information Fusion in Biological Network Inference

Title:Information Fusion in Biological Network Inference

Volume: 13 Issue: 2

Author(s): Davide Angeli and Jesus S. Aguilar-Ruiz*

Affiliation:

• Computer Science, Universidad Pablo de Olavide, ES-41013 Seville,Spain

Keywords: Biological networks, protein-protein interaction network, gene regulatory network, prior knowledge, Gene Ontology, semantic similarity measure, HRSS.

Abstract: Background: Biological networks are used to represent interactions involving genes, DNA, RNA and proteins that are able to manipulate many cellular processes.

Objective: The aim of this study is to evaluate whether prior knowledge can improve the quality of biological networks, in particular protein-protein interaction networks and gene regulatory networks.

Method: Gene Ontology (GO) as well as three different types of semantic similarity measures were used to assess the interaction between biological networks so as to build the corresponding filtered networks. Both the original and the filtered networks were statistically compared against a reference network.

Results and Conclusion: The results confirm the effectiveness of the GO-based measure HRSS as it improves the quality of the original network by removing many false interactions while maintaining the true interactions. In general, the inclusion of external sources of biological information to improve the quality of inferred knowledge (networks or any other model) is a fundamental step before the fusion of filtered -statistically validated- intermediate results.

Cite this article as:

Angeli Davide and Aguilar-Ruiz S. Jesus *, Information Fusion in Biological Network Inference, Current Bioinformatics 2018; 13 (2) . https://dx.doi.org/10.2174/1574893612666170718145952