MTAGCN: predicting miRNA-target associations in Camellia sinensis var. assamica through graph convolution neural network.

ABSTRACT:

Background

MircoRNAs (miRNAs) play a central role in diverse biological processes of Camellia sinensis var.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.

Results

In 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, n

SUBMITTER: Feng H 

PROVIDER: S-EPMC9275082 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature