Improved Biological Network Reconstruction Using Graph Laplacian Regularization

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Abstract
Biological networks reconstruction is a crucial step towards the functional characterization and elucidation of living cells. Computational methods for inferring the structure of these networks are of paramount importance since they provide valuable information regarding organization and behavior of the cell at a system level and also enable careful design of wet-lab experiments. Despite many recent advances, according to the scientific literature, there is room for improvements from both the efficiency and the accuracy point of view in link prediction algorithms. In this article, we propose a new method for the inference of biological networks that makes use of a notion of similarity between graph vertices within the framework of graph regularization for ranking the links to be predicted. The proposed approach results in more accurate classification rates in a wide range of experiments, while the computational complexity is reduced by two orders of magnitude with respect to many current state-of-the-art algorithms.

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