WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning
Fig 4
Weights given to constituent algorithm predictions by WORMHOLE SVMs are correlated across species comparisons.
(A) Distribution of Pearson correlations between weight vectors across models trained on different pairs of query and target species show reasonable concordance across species pairs (mean = 0.54), with considerable variation (standard deviation = 0.21) indicating species-pair-specific structure in the models. (B) Box and whisker plot of the weights given to predictions made by each constituent algorithm by WORMHOLE SVMs trained on each pair of query and target species show that each constituent algorithm has relatively consistent weight within each species pair comparison. Note that PANTHER has the highest average weight, as expected. Ortholog prediction methods are ordered by median SVM weight.