Jiajie Peng1,2,3, Weiwei Hui1, Qianqian Li1, Bolin Chen1,2,3, Jianye Hao4, Qinghua Jiang5, Xuequn Shang1,2, Zhongyu Wei6. 1. School of Computer Science, Northwestern Polytechnical University, Xi'an, China. 2. Key Laboratory of Big Data Storage and Management, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi'an, China. 3. Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science, Northwestern Polytechnical University, Xi'an, China. 4. College of Intelligence and Computing, Tianjin University, Tianjin, China. 5. School of Life Science and Technology, Harbin Institute of Technology, Harbin, China. 6. School of Data Science, Fudan University, Shanghai, China.
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/Issingjessica/MDA-CNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/Issingjessica/MDA-CNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.