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In order for the predicted interactions to be directly adopted by biologists,the ma- chine learning predictions have to be of high precision, regardless ofrecall. This aspect cannot be evaluated or numerically represented well bytraditional metrics like accuracy, ROC, or precision-recall curve. In thiswork, we start from the alignment in sensitivity of ROC and recall ofprecision-recall curve, and propose an evaluation metric focusing on theability of a model to be adopted by biologists. This metric evaluates theability of a machine learning algorithm to predict only new interactions,meanwhile, it eliminates the influence of test dataset. In the experiment ofevaluating different classifiers with a same data set and evaluating the samepredictor with different datasets, our new metric fulfills the evaluation taskof our interest while two widely recognized metrics, ROC and precision-recallcurve fail the tasks for different reasons.

Evaluating Protein-protein Interaction Predictors with a Novel 3-Dimensional Metric