A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference

Abstract

Inferring the relationship among proteins is a central issue of computational biology and a diversity of biological assays are utilized to predict the relationship. However, as experiments are usually expensive to perform, automatic data selection is employed to reduce the data collection cost. Although data useful for link prediction are different in each local sub-network, existing methods cannot select different data for different processes. This article presents a new algorithm for inferring biological networks from multiple types of assays. The proposed algorithm is based on transfer learning and can exploit local information effectively. Each assay is automatically weighted through learning and the weights can be adaptively different in each local part. The authors’ algorithm was favorably examined on two kinds of biological networks: a metabolic network and a protein interaction network. A statistical test confirmed that the weight that our algorithm assigned to each assay was meaningful.