Predicting miRNA-disease associations based on PPMI and attention network

• Published in: BMC bioinformatics 2023-03, Vol.24 (1), p.113-113, Article 113
• Main Authors: Xie, Xuping, Wang, Yan, He, Kai, Sheng, Nan
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial neural networks; Attention network; Biotechnology; Boolean; Cancer; Computational Biology - methods; Computer applications; Deep learning; Disease; Experimental methods; Gene Regulatory Networks; Genetic Predisposition to Disease; Humans; Machine learning; Methods; MicroRNA; MicroRNAs; MicroRNAs - genetics; miRNA; MiRNA-disease association prediction; Neighborhoods; Neural networks; Neural Networks, Computer; Pathogenesis; Performance evaluation; Polymerase chain reaction; PPMI; Proximity; Random walk; Representations; Research methodology; ROC Curve; Semantics; Similarity; Testing
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05152-z

With the development of biotechnology and the accumulation of theories, many studies have found that microRNAs (miRNAs) play an important role in various diseases. Uncovering the potential associations between miRNAs and diseases is helpful to better understand the pathogenesis of complex diseases. However, traditional biological experiments are expensive and time-consuming. Therefore, it is necessary to develop more efficient computational methods for exploring underlying disease-related miRNAs. In this paper, we present a new computational method based on positive point-wise mutual information (PPMI) and attention network to predict miRNA-disease associations (MDAs), called PATMDA. Firstly, we construct the heterogeneous MDA network and multiple similarity networks of miRNAs and diseases. Secondly, we respectively perform random walk with restart and PPMI on different similarity network views to get multi-order proximity features and then obtain high-order proximity representations of miRNAs and diseases by applying the convolutional neural network to fuse the learned proximity features. Then, we design an attention network with neural aggregation to integrate the representations of a node and its heterogeneous neighbor nodes according to the MDA network. Finally, an inner product decoder is adopted to calculate the relationship scores between miRNAs and diseases. PATMDA achieves superior performance over the six state-of-the-art methods with the area under the receiver operating characteristic curve of 0.933 and 0.946 on the HMDD v2.0 and HMDD v3.2 datasets, respectively. The case studies further demonstrate the validity of PATMDA for discovering novel disease-associated miRNAs.