DGA algorithm performance.

<p>The highest F-measure from cross-validation results from the DGA algorithm with different disease relationships calculated using the Jaccard, Simpson, Geometric, and Cosine indices.</p

Summarization of standard association indices for calculating disease relationships.

<p>Summarization of standard association indices for calculating disease relationships.</p

List of genes that met the selection criteria with coverage value of a gene greater than or equal to 70 and an association score greater than or equal to 40.

<p>List of genes that met the selection criteria with coverage value of a gene greater than or equal to 70 and an association score greater than or equal to 40.</p

Comparing performance of our method and random experiment.

<p>The disease-gene association predictions using DGA algorithm with disease relationships from the Jaccard index and random experiments with different values of <i>k</i> were compared.</p

List of predicted disease and gene pairs that were not evident in the gold standard.

<p>List of predicted disease and gene pairs that were not evident in the gold standard.</p

Receiver operating characteristic curves for the prediction of disease-gene associations using interactions from protein complexes.

<p>The disease-gene prediction results using our DGA algorithm with interactions from protein complexes obtained from the CORUM database.</p

Receiver operating characteristic curves for the predictions of disease-gene association.

<p>The disease-gene prediction results at the optimal parameter <i>k</i> = 18.</p

Clustering of the chemokine receptors and their interactors.

<p>Clustering of the chemokine receptors and their interactors.</p

Pairs of Pfam domain sets showing significant^* enrichment of predicted interactions.

<p>^*p≤0.1 only; t≥1 for both pos/neg - no zeroes.</p><p>Pairs of Pfam domain sets showing significant^{<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003814#nt101" target="_blank">*</a>} enrichment of predicted interactions.</p

Workflow.

<p>Images of a genome-wide cellular RNAi knockdown screen (the screening data was derived from the Mitocheck project, <a href="http://www.mitocheck.org" target="_blank">www.mitocheck.org</a>) were segmented and their features extracted to compile pairwise phenotype descriptors for a large set of gene pairs. These descriptors were used to train a machine learning system to discriminate activating and inhibiting PPIs taken from a reference. The performance was evaluated using cross-validation. The trained SVM models were used to predict the effects of uncharacterized PPIs. In addition, the SVM models were used to estimate similarity of the effects of proteins for all combinations of protein pairs in the network. Subsequently, this Effect Similarity Rate (ESR) was exemplarily used for clustering of functionally related protein sub-networks.</p