Abstract
The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network. However, existing methods often suffer when the number of labeled examples is low or when no negative examples are available. In this paper we propose a multi-task method that is able to simultaneously reconstruct the human and the mouse GRNs using the similarities between the two. This is done by exploiting, in a transfer learning approach, possible dependencies that may exist among them. Simultaneously, we solve the issues arising from the limited availability of examples of links by relying on a novel clustering-based approach, able to estimate the degree of certainty of unlabeled examples of links, so that they can be exploited during the training together with the labeled examples. Our experiments show that the proposed method can reconstruct both the human and the mouse GRNs more effectively compared to reconstructing each network separately. Moreover, it significantly outperforms three state-of-the-art transfer learning approaches that, analogously to our method, can exploit the knowledge coming from both organisms. Finally, a specific robustness analysis reveals that, even when the number of labeled examples is very low with respect to the number of unlabeled examples, the proposed method is almost always able to outperform its single-task counterpart.
Highlights
The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years
The approach proposed in this paper can be considered the first attempt to employ the Predictive Clustering Tree (PCT) multi-target prediction method implemented in CLUS to work in a multi-task learning setting, where the variables to be predicted are associated with two different tasks
Since we work in the positive-unlabeled learning setting where no negative examples are a vailable[16,17,58], we evaluate the performance of different methods in terms of recall@k and the area under the recall@k curve (AUR@K)
Summary
The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. In this paper we propose a multi-task method that is able to simultaneously reconstruct the human and the mouse GRNs using the similarities between the two This is done by exploiting, in a transfer learning approach, possible dependencies that may exist among them. Our experiments show that the proposed method can reconstruct both the human and the mouse GRNs more effectively compared to reconstructing each network separately It significantly outperforms three state-of-theart transfer learning approaches that, analogously to our method, can exploit the knowledge coming from both organisms. Since tumor cells are mainly caused by the expression of genes outside the original context of the cell, the understanding of gene regulation mechanisms appears to be fundamental to study various forms of cancer[1,2] In this context, the analysis of Gene Regulatory Networks (GRNs) appears to be a fundamental task. The task at hand is referred to as “reverse-engineering” or “gene network reconstruction”
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