Exploiting Transfer Learning for the Reconstruction of the Human Gene Regulatory Network

Motivation: The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data has received increasing attention in recent years, due to its usefulness in the understanding of regulatory mechanisms involved in human diseases. Most of the existing methods reconstruct the network through machine learning approaches, by analyzing known examples of interactions. However, i) they often produce poor results when the amount of labeled examples is limited, or when no negative example is available and ii) they are not able to exploit information extracted from GRNs of other (better studied) related organisms, when this information is available. Result: In this paper we propose a novel machine learning method which overcomes these limitations, by exploiting the knowledge about the GRN of a source organism for the reconstruction of the GRN of the target organism, by means of a novel transfer learning technique. Moreover, the proposed method is natively able to work in the Positive-Unlabeled setting, where no negative example is available, by fruitfully exploiting a (possibly large) set of unlabeled examples. In our experiments we reconstructed the human GRN, by exploiting the knowledge of the GRN of M. musculus. Results showed that the proposed method outperforms state-of-the-art approaches and identifies previously unknown functional relationships among the analyzed genes.