Function-function correlated multi-label protein function prediction over interaction networks.

Many previous works in protein function prediction make predictions one function at a time, fundamentally, which assumes the functional categories to be isolated. However, biological processes are highly correlated and usually intertwined together to happen at the same time; therefore, it would be beneficial to consider protein function prediction as one indivisible task and treat all the functional categories as an integral and correlated prediction target. By leveraging the function-function correlations, it is expected to achieve improved overall predictive accuracy. To this end, we develop a network-based protein function prediction approach, under the framework of multi-label classification in machine learning, to utilize the function-function correlations. Besides formulating the function-function correlations in the optimization objective explicitly, we also exploit them as part of the pairwise protein-protein similarities implicitly. The algorithm is built upon the Green's function over a graph, which not only employs the global topology of a network but also captures its local structures. In addition, we propose an adaptive decision boundary method to deal with the unbalanced distribution of protein annotation data. Finally, we quantify the statistical confidence of predicted functions to facilitate post-processing of proteomic analysis. We evaluate the proposed approach on Saccharomyces cerevisiae data, and the experimental results demonstrate very encouraging results.