A) An illustration of connectivity homology (CH). Each node is described by two parameters (degree [deg.] and betweenness centrality [bet.cent.]) at three levels: low, medium, and high.

<p>Certain nodes have the same vectors (Node B/Node 2/Node 3); these nodes can be said to be <i>connectivity homologous</i> (CH). Other nodes do not (Node A/Node 1); these are non-connectivity homologous (non-CH). B) Schematic of the SINaTRA algorithm. We begin with the PPI networks of both our source and target species, calculate the network parameters (independently), and normalize the values of all parameters. Next, we use machine learning methods on the normalized network parameters of our source species as well as experimentally derived labels of synthetic lethality to construct a species-independent model of SL. Finally, we apply this model to the normalized network data of our target species in order to attain SL predictions in our target.</p

Within-function enrichment of putative SL pairs based on gene product interactions.

<p>*p<0.05, without multiplicity correction</p><p>†: p<0.001, with multiplicity correction</p><p><i>Complex</i> describes all gene pairs that are within the same pathway. <i>Other</i> represents all pairs that have another described PPI. <i>Parallel</i> refers to all pairs with no known PPI between them. Interactions are determined using Reactome data.</p

Methods of network parameter normalization.

<p>Methods of network parameter normalization.</p

The landscape of human synthetic lethality.

<p>This network depicts all gene pairs with SINaTRA score ≥0.95 (1,229 SL pairs) that map to Reactome pathways (458 pairs). Here, each hexagon represents one high-level pathway designation in Reactome. Larger nodes indicate more SL pairs with that designation. Within the hexagonal nodes, we show the networks of synthetic lethality where both members have the same function in Reactome. Each node is a gene and an edge represents a predicted SL interaction. Gene nodes are weighted by degree and colored by closeness centrality. In turn, weighted edges join hexagonal nodes if pathway-divergent pairs exist; that is, one member of the pair is of one pathway while the second member is of the other. Edges are weighted by the number of pathway-divergent gene pairs associated with both pathways.</p

Network parameters used in our model.

<p>A single-node network parameter provides two values to the feature vector per pair (8 single-node parameters create 16 values per pair). Each node-pair parameter contributes one value describing that pair. Parameter importance is measured using Gini importance[<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004506#pcbi.1004506.ref017" target="_blank">17</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004506#pcbi.1004506.ref018" target="_blank">18</a>] in the NetworkX Python package.[<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004506#pcbi.1004506.ref019" target="_blank">19</a>]</p

Function-specific patterns of synthetic lethality.

<p>The heatmap represents the ratio of median parameters for the SL pairs of a given function versus all pairs of a given function. For example, the SL pairs of Signal Transduction have two-times greater values for inverse shortest path than for the non-SL pairs of Signal Transduction. Rows are clustered by node-pair parameter values (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004506#pcbi.1004506.t001" target="_blank">Table 1</a>). Parameter variance is plotted above the heat map. Single-node parameters (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004506#pcbi.1004506.t001" target="_blank">Table 1</a>) are consistently altered in SL regardless of function. However, node-pair parameters differ between functions. This distinction suggests that network substructure may dictate SL mechanisms associated with a specific function.</p

The top ten highest scoring within-pathway, putative SL gene pairs.

<p>The top ten highest scoring within-pathway, putative SL gene pairs.</p

A. Receiver operating characteristic (ROC) curves for classification of SL/non-SL gene in <i>S</i>. <i>pombe</i> using <i>S</i>. <i>cerevisiae</i> as source.

<p>Comparison of untranslated (“raw”) parameters (gray, AUC = 0.67) and the translated parameters used in SINaTRA (red, AUC = 0.86). B. ROC curve of SL predictions using SINaTRA (AUC = 0.86) compared functional homology of gene pair products (AUC = 0.81) and gene homology (AUC = 0.60). The model based on gene homology was created using only gene pairs with homology data. C. Positive predictive value (PPV) of all (dark gray) and within-complex (red) gene pairs. When accounting for the expected ratio of SL:NSL (1:1000), a SINaTRA score threshold of 0.95 yields a median PPV of 17% (a 170-fold increase over what is expected by chance). At 0.85, the PPV drops to 7%. PPV increases in within-complex gene pairs, suggesting that this may be a good initial filter for experimental validation. D. At each SINaTRA score cutoff, we plot the number of experimentally identified SL pairs in that bin (red), as well as the number we expect to find at each level (gray). E. SINaTRA scores of all human predictions, as well as pairs predicted or found to be SL in two datasets: DAISY and Syn-Lethality. F. We compare the predictive ability of SINaTRA score to identify genes belonging to DAISY (tested) and Syn-Lethality datasets compared to functional similarity and homology.</p

Precision of the different methods in test set 4 with all the interactions described in the reference standard Drugs.com (high and moderate clinically significant interactions).

<p>Precision of the different methods in test set 4 with all the interactions described in the reference standard Drugs.com (high and moderate clinically significant interactions).</p

Example of some arrhythmia DDIs described in Drugdex and detected by the different similarity-based models (2D MACCS, 3D similarity, ADEPF, TPF, DDIPF and ATC)

<p>In the table we provided also proportional reporting ratio values (PRR) found in TWOSIDES data.</p><p>.</p