Integration of Gene Ontology-based similarities for supporting analysis of protein-protein interaction networks

Authors:
Haiying Wang;Huiru Zheng;Fiona Browne;David H. Glass;Francisco Azuaje
Affiliations:
School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Newtownabbey, Co. Antrim, BT37 0QB, UK;School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Newtownabbey, Co. Antrim, BT37 0QB, UK;i-Path Diagnostics, 23 University Street, Belfast BT 7 1FY, UK;School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Newtownabbey, Co. Antrim, BT37 0QB, UK;Department of Cardiovascular Diseases, CRP-Santé, L-1150, Luxembourg
Venue:
Pattern Recognition Letters
Year:
2010

Citing 7
Cited 1

Probabilistic Networks and Expert Systems

Probabilistic Networks and Expert Systems
An Information-Theoretic Definition of Similarity

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Gene-Ontology-based clustering of gene expression data

Bioinformatics
Modularized learning of genetic interaction networks from biological annotations and mRNA expression data

Bioinformatics
Ontological analysis of gene expression data: current tools, limitations, and open problems

Bioinformatics
The relationship between Precision-Recall and ROC curves

ICML '06 Proceedings of the 23rd international conference on Machine learning
Predictive Integration of Gene Ontology-Driven Similarity and Functional Interactions

ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops

An information theoretic approach to assessing gene-ontology-driven similarity and its application

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.10

Visualization

Abstract

In recent years there has been a growing trend towards the inclusion of diverse genomic information to support comprehensive large-scale prediction of protein-protein interaction networks. The Gene Ontology (GO) is one such functional knowledge resource, which consists of three hierarchies to describe functional attributes of gene products: Molecular function, biological process, and cellular component. Using Bayesian networks, this paper presents a framework for the probabilistic combination of semantic similarity knowledge extracted from the three GO hierarchies for analysis of protein-protein interaction networks and demonstrates its application in yeast. The results indicate that by integrating information encoded in the GO hierarchies a better result can be achieved in terms of both statistical prediction capability and potential biological relevance.