Structural Attention Mechanism Auto-encoder for miRNA-disease Association Prediction

doi:10.3969/j.issn.1006-2475.2024.04.018

Abstract

Abstract:
Abstract： Research on MicroRNA （miRNA） -disease association prediction is helpful for human disease prevention， diagnosis and treatment， etc. Many researchers have developed miRNA-disease association prediction methods based on graph auto-encoder. However， most of the auto-encoder methods do not consider the difference between neighbor nodes when coding the central node. Therefore， this paper proposes a new method called structural attention mechanism auto-encoder for miRNA-disease association prediction （SAAE）. The SAAE model uses an encoder based on graph neural network， which uses multiple coding layers to explore the information of multi-order neighbors. In order to fuse the feature information of the central node and the neighbor node with different weights and capture the structure information of the node in the graph， the structured attention mechanism is introduced in the coding layer to encode the original information of the graph node to generate new feature information. Subsequently， the decoding is performed by a decoder， and the decoded feature information is mined for potential associations between miRNA and disease nodes using a random forest algorithm. The experimental results show that the average area under the curve of SAAE under five-fold cross validation is 94.53%. In addition， two case studies on kidney tumors and lungtumors were conducted to verify the validity of SAAE prediction.

Key words: Key words： miRNA-disease association, graph auto-encoder, attention mechanism, structural information

CLC Number:

TP391

XIE Guobo, LUO Canjie, LIN Zhiyi, JIANG Zelin. Structural Attention Mechanism Auto-encoder for miRNA-disease Association Prediction[J]. Computer and Modernization, 2024, 0(04): 107-114.

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