Summary of the number of instances of the test set correctly classified using the F and F′ Normal combined models.

<p>numb., number.</p

Graphics of classification of the test sets using the F and F′ Normal combined models.

<p>The score represents the classification of the instances as PPI. The instances were classified as PPIs or no-PPIs, and no-PPIs classification scores were converted to interaction scores. “A”, classification of the F test set using the F Normal combined model; “B”, classification of the F′ test set using the F′ Normal combined model; Red, PPIs instances; Gray, no-PPIs instances.</p

Mann-Whitney U statistical test applied to performance measures of models generated from Normal training datasets.

*<p>, no significant difference.</p

Predictive performance of the machine learning using the Normal training datasets.

<p>The letters F, C and F+C indicate that the Normal training datasets originated from the feature descriptors “frequency”, “composition” and “frequency” plus “composition”, respectively. The prime symbol indicates the Normal training datasets formed using the symmetrical attributes of the previously mentioned datasets (details in the “Material and Methods” section). The numbers at the bottom of the boxes are the medians for each dataset.</p

Descriptions of instances inserted in the test set.

<p>numb, numbers.</p>*<p>, indicates PPIs and no-PPIs within the same species.</p>**<p>, indicates PPIs and no-PPIs among different species.</p><p>-, numbers not showed.</p

The Development of a Universal <i>In Silico</i> Predictor of Protein-Protein Interactions

<div><p>Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal <i>In Silico</i> Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation.</p></div

Synthesis of several decision trees generated during the ML training.

<p>The aa at the top of each box is relative to the attributes specified at the first and second level of the trees. A, B, C and D indicate the low, moderate, high and very high bins, respectively (for more details, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065587#pone-0065587-t003" target="_blank">Table 3</a> and the “<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065587#s4" target="_blank">Materials and Methods</a>” section). Green boxes, a combination that classifies an instance as a PPI; Red boxes, a combination that classifies an instance as a no-PPI; Brown boxes, a combination that classifies an instance as a PPI or no-PPI; “?”, a combination for which an instance can not be classified, requiring classification at the next level of the tree.</p

Detailed analysis of the classification of the test set using the F′ Normal combined model.

<p>The values in the right side of each table and the score over each bar indicate the number and percentages of instances correctly classified, respectively. Single species, indicates PPIs and no-PPIs within the same species; Different species, indicates PPIs and no-PPIs among different species; *, groups that include instances of parasite-host associations.</p

Descriptions of the bins used to discretize the attributes of the F and C datasets.

<p>avg, average; stdv, standard deviation.</p

A general workflow of the procedures adopted in this work.

<p>A general workflow of the procedures adopted in this work.</p