Paper Info
Title
Using Topology Information for Protein-Protein Interaction Prediction
Abstract
The reconstruction of protein-protein interaction networks is nowadays an important challenge in systems biology. Computational approaches can address this problem by complementing high-throughput technologies and by helping and guiding biologists in designing new laboratory experiments. The proteins and the interactions between them form a network, which has been shown to possess several topological properties. In addition to information about proteins and interactions between them, knowledge about the topological properties of these networks can be used to learn accurate models for predicting unknown protein-protein interactions. This paper presents a principled way, based on Bayesian inference, for combining network topology information jointly with information about proteins and interactions between them. The goal of this combination is to build accurate models for predicting protein-protein interactions. We define a random graph model for generating networks with topology similar to the ones observed in protein-protein interaction networks. We define a probability model for protein features given the absence/presence of an interaction and combine this with the random graph model by using Bayes' rule, to finally arrive at a model incorporating both topological and feature information.
Year
DOI
Venue
2014
10.1007/978-3-319-09192-1_2
PRIB
Keywords
Field
DocType
bayesian methods,network analysis,protein-protein interaction,protein protein interaction
Protein–protein interaction prediction,Random graph,Bayesian inference,Computer science,Systems biology,Network topology,Artificial intelligence,Bioinformatics,Network analysis,Machine learning,Bayesian probability,Bayes' theorem
Conference
Volume
ISSN
Citations 
8626
0302-9743
3
PageRank 
References 
Authors
0.36
19
2
Name
Order
Citations
PageRank
Adriana Birlutiu1706.41
Tom Heskes21519198.44