Network Propagation with Dual Flow for Gene Prioritization

<div><p>Based on the hypothesis that the neighbors of disease genes trend to cause similar diseases, network-based methods for disease prediction have received increasing attention. Taking full advantage of network structure, the performance of global distance measurements is generally superior to local distance measurements. However, some problems exist in the global distance measurements. For example, global distance measurements may mistake non-disease hub proteins that have dense interactions with known disease proteins for potential disease proteins. To find a new method to avoid the aforementioned problem, we analyzed the differences between disease proteins and other proteins by using essential proteins (proteins encoded by essential genes) as references. We find that disease proteins are not well connected with essential proteins in the protein interaction networks. Based on this new finding, we proposed a novel strategy for gene prioritization based on protein interaction networks. We allocated positive flow to disease genes and negative flow to essential genes, and adopted network propagation for gene prioritization. Experimental results on 110 diseases verified the effectiveness and potential of the proposed method.</p></div

Leukoencephalopathy with Vanishing White Matter Protein-Protein Interaction Network.

<p>Leukoencephalopathy with Vanishing White Matter Protein-Protein Interaction Network.</p

One tailed <i>t</i>-Tests for Table 6: <i>NP</i>_{<i>D</i>&<i>E</i>} versus Competing Approaches.

<p>One tailed <i>t</i>-Tests for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116505#pone.0116505.t006" target="_blank">Table 6</a>: <i>NP</i>_{<i>D</i>&<i>E</i>} versus Competing Approaches.</p

An example of gene prioritization based on network.

<p>(a) The disease proteins <i>a</i> and <i>b</i> are selected as the training set, while <i>c</i> as the test disease protein. (b) Global distance measurements may mistake the non-disease hub protein <i>e</i> for a disease protein.</p

ROC curves.

<p>ROC curves.</p

Networks used in this work.

<p>Networks used in this work.</p

Statistics of the genes in the protein interaction network constructed based on the STRING database.

<p>Statistics of the genes in the protein interaction network constructed based on the STRING database.</p

Statistics of the proteins in the protein interaction network constructed based on the i2d database.

<p>Statistics of the proteins in the protein interaction network constructed based on the i2d database.</p

Statistics of the performance (the average values of enrichment score) with disease as a unit.

<p>Statistics of the performance (the average values of enrichment score) with disease as a unit.</p

Median values of the proportions of non-disease essential proteins among <i>n</i> (<i>n</i> ∈ {1, 2, …, 12}) neighbors in the protein interaction network constructed based on the i2d database.

<p>Median values of the proportions of non-disease essential proteins among <i>n</i> (<i>n</i> ∈ {1, 2, …, 12}) neighbors in the protein interaction network constructed based on the i2d database.</p