The integration of weighted gene association networks based on information entropy.

Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort

Comparisons of overlapped edges and weights information of the networks with that of the GO network.

<p>(A)The fraction of overlapped edges of the INet network and the four original networks with the GO network. (B) The Pearson correlation coefficient between the vectors for the overlapped edge weights of the network and the GO network.</p

Training process to determine adjustment parameter <i>θ</i>.

<p>Training process to determine adjustment parameter <i>θ</i>.</p

The number of overlapped nodes and edges of the four original networks under study.

<p>(A)Common nodes of the four original networks (HIPPIE, HumanNet, FunCoup and STRING). (B)Common edges of the four original networks.</p

Performance comparisons of different networks in disease gene prediction by leave-one-out cross validation.

<p>(A) The integrated network is constructed from String and HumanNet; (B) The integrated network is constructed from STRING and HIPPIE. (C) The integrated network is constructed from STRING and FunCoup. (D)The integrated network is constructed from HIPPIE and FunCoup.</p

Performance comparison of disease gene prediction based on the integrated network INet, the four original networks and GO network.

<p>(A)Percentage of the test genes ranked within top 100. (B) ROC curves and AUC values for the prediction results.</p

Additional file 1: of The integration of weighted human gene association networks based on link prediction

The Integration of Weighted Human Gene Association Networks Based on Link Prediction. Table S1. The seed genes associated with obesity obtained from OMIM. Table S2. The test genes for obesity associated genes prediction. Figure S1. The adjustment of parameter Îą in quasi-local similarity indices by link prediction accuracy measured by precision. (DOCX 57 kb

Performance comparison of obesity disease gene prediction based on different networks.

<p>(A) Percentage of the test genes ranked within top 100. (B) ROC curves and AUC values for the prediction of test genes.</p