Author:
Feng Haisong,Xiang Ying,Wang Xiaosong,Xue Wei,Yue Zhenyu
Abstract
AbstractBackgroundMircoRNAs (miRNAs) play a central role in diverse biological processes ofCamellia sinensisvar.assamica (CSA) through their associations with target mRNAs, including CSA growth, development and stress response. However, although the experiment methods of CSA miRNA-target identifications are costly and time-consuming, few computational methods have been developed to tackle the CSA miRNA-target association prediction problem.ResultsIn this paper, we constructed a heterogeneous network for CSA miRNA and targets by integrating rich biological information, including a miRNA similarity network, a target similarity network, and a miRNA-target association network. We then proposed a deep learning framework of graph convolution networks with layer attention mechanism, named MTAGCN. In particular, MTAGCN uses the attention mechanism to combine embeddings of multiple graph convolution layers, employing the integrated embedding to score the unobserved CSA miRNA-target associations.DiscussionComprehensive experiment results on two tasks (balanced task and unbalanced task) demonstrated that our proposed model achieved better performance than the classic machine learning and existing graph convolution network-based methods. The analysis of these results could offer valuable information for understanding complex CSA miRNA-target association mechanisms and would make a contribution to precision plant breeding.
Funder
National Natural Science Foundation of China
Natural Science Young Foundation of Anhui
Natural Science Young Foundation of Anhui Agricultural University
Introduction and Stabilization of Talent Project of Anhui Agricultural University
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
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