COSNet: An R package for label prediction in unbalanced biological networks

Abstract

Several problems in computational biology and medicine are modeled as learning problems in graphs, where nodes represent the biological entities to be studied, e.g. proteins, and connections different kinds of relationships among them, e.g. protein–protein interactions. In this context, classes are usually characterized by a high imbalance, i.e. positive examples for a class are much less than those negative. Although several works studied this problem, no graph-based software designed to explicitly take into account the label imbalance in biological networks is available. We propose COSNet, an R package to serve this purpose. COSNet deals with the label imbalance problem by implementing a novel parametric model of Hopfield Network (HN), whose output levels and activation thresholds of neurons are parameters to be automatically learnt. Due to the quasi-linear time complexity, COSNet nicely scales when the number of instances is large, and application examples to challenging problems in biomedicine show the efficiency and the accuracy of the proposed library.