A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks.

ABSTRACT: Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs, with nodes representing bio-entities, such as molecules, and edges representing relations among them, such as interactions. Due to the current availability of a huge amount of biological data, it is very important to consider in silico analysis methods based on, for example, machine learning, that could take advantage of the inner graph structure of the data in order to improve the quality of the results. In this scenario, graph neural networks (GNNs) are recent computational approaches that directly deal with graph-structured data. In this paper, we present a GNN network for the analysis of siRNA-mRNA interaction networks. siRNAs, in fact,

SUBMITTER: La Rosa M 

PROVIDER: S-EPMC9696923 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature