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

Abstract

AbstractMotivationA 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.ResultsWe propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures richer interaction features between diseases and miRNAs based on a three-layer network with an additional gene layer. 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.AvailabilityThe source code and data are available at https://github.com/Issingjessica/MDA-CNN.Contactjiajiepeng@nwpu.edu.cn;shang@nwpu.edu.cn;zywei@fudan.edu.cnSupplementary informationSupplementary data are available at Bioinformatics online.