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Bioinformatics. 2019 Apr 12. pii: btz254. doi: 10.1093/bioinformatics/btz254. [Epub ahead of print]

A learning-based framework for miRNA-disease association identification using neural networks.

Peng J1,2,3, Hui W1, Li Q1, Chen B1,2,3, Hao J4, Jiang Q5, Shang X1,2, Wei Z6.

Author information

School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, China.
Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
College of Intelligence and Computing, Tianjin University, Tianjin, China.
School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.
School of Data Science, Fudan University, Shanghai, China.



A microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many biological processes. Lots of studies have shown that miRNAs are implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes.


We propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures interaction features between diseases and miRNAs based on a three-layer network including disease similarity network, miRNA similarity network and protein-protein interaction network. Then, it employs an auto-encoder to identify the essential feature combination for each pair of miRNA and disease automatically. Finally, taking the reduced feature representation as input, it uses a convolutional neural network to predict the final label. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and miRNA-phenotype association prediction.


The source code and data are available at


Supplementary data are available at Bioinformatics online.

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