%0 Journal Article
%T Protein Networks Reveal Detection Bias and Species Consistency When Analysed by Information-Theoretic Methods
%A Luis P. Fernandes
%A Alessia Annibale
%A Jens Kleinjung
%A Anthony C. C. Coolen
%A Franca Fraternali
%J PLOS ONE
%D 2012
%I Public Library of Science (PLoS)
%R 10.1371/journal.pone.0012083
%X We apply our recently developed information-theoretic measures for the characterisation and comparison of protein每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每scale analysis of protein每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每two每hybrid methods are sufficiently consistent to allow for intra每species comparisons (between different experiments) and inter每species comparisons, while data from affinity每purification mass每spectrometry methods show large differences even within intra每species comparisons.
%U http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012083