Department of Computer Science, University of Bari Aldo Moro, Bari, 70125, Italy.
Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, 1000, Slovenia.
Sci Rep. 2020 Dec 18;10(1):22295. doi: 10.1038/s41598-020-78033-7.
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.
近年来，基于机器学习方法从基因表达数据中重建基因调控网络（GRNs）受到了越来越多的关注。当前的任务是识别网络中基因之间的调控关系。然而，当标记的示例数量较少或没有负示例时，现有的方法往往会遇到困难。在本文中，我们提出了一种多任务方法，该方法能够利用两种网络之间的相似性，同时重建人类和小鼠的 GRNs。这是通过在迁移学习方法中利用可能存在的依赖关系来实现的。同时，我们通过一种新的基于聚类的方法解决了由于链接的示例数量有限而产生的问题，该方法能够估计链接的未标记示例的置信度，以便在训练过程中与标记示例一起利用它们。我们的实验表明，与分别重建每个网络相比，所提出的方法可以更有效地重建人类和小鼠的 GRNs。此外，它还显著优于三种最先进的迁移学习方法，这些方法类似于我们的方法，可以利用来自两种生物的知识。最后，特定的稳健性分析表明，即使标记的示例数量相对于未标记的示例数量非常少，所提出的方法也几乎总是能够优于其单任务对应方法。
