PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods
Luis Fernandes, Alessia Annibale, Jens Kleinjung, Anthony (Ton) C C Coolen and Franca Fraternali
PLoS ONE Volume 5, e12083, 2010.

Abstract

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7659
Deposited By:Anthony (Ton) C C Coolen
Deposited On:17 March 2011