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

Haohan Wang; Madhavi K. Ganapathiraju

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arXiv · 1511.02196

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

Haohan Wang·Madhavi K. Ganapathiraju

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Abstract

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

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Haohan Wang, Madhavi K. Ganapathiraju. 2015-11-06. Evaluating Protein-protein Interaction Predictors with a Novel 3-Dimensional Metric. https://arxiv.org/abs/1511.02196

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