MiRNA-disease association prediction method and device based on graph neural network fusion multi-view information

Abstract

The invention discloses a miRNA-disease association prediction method and device based on graph neural network fusion multi-view information, and the method integrates miRNA-disease-related multi-omics data to construct a plurality of views, not only considers a plurality of homogeneous similarity networks, but also considers a heterogeneous bipartite network, extracts node features on all views in combination with a graph neural network and multi-view learning, captures dependency between global features and local features through a discriminator, and can better capture the complex nonlinear relation between miRNA and diseases.

Description

technical field [0001] The present invention relates to the field of biological artificial intelligence, in particular to a miRNA-disease association prediction method and device based on graph neural network fusion of multi-view information. Background technique [0002] MicroRNA (miRNA) is an important class of small non-coding RNA molecules that regulate gene expression by degrading mRNA or inhibiting mRNA translation. Accumulating evidence shows that miRNAs play crucial roles in various cancer-related pathways. Therefore, identifying miRNA-disease associations may provide new directions for understanding the underlying pathogenic mechanisms of diseases. [0003] Using biological experiments to identify miRNA-disease associations usually has a high accuracy rate, but it requires a lot of resources and time. In recent years, with the emergence and development of the Human Genome Project and high-throughput sequencing technology, various biological omics data have grown e...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a miRNA-disease association prediction method and device based on graph neural network fusion of multi-view information, aiming at combining graph neural network and multi-view learning to solve the problem of predicting miRNA-disease The disease association calculation method cannot make full use of the complementary information of multi-omics data, it is difficult to capture the complex nonlinear relationship between miRNA and disease, and the problem of low prediction accuracy

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