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

Abstract

MotivationA microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many bio-logical processes. Lots of studies have shown that miRNAs were 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.\n\nResultsWe proposed 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. An auto-encoder is then employed to identify the essential feature combination for each pair of miRNA and disease automatically. A final label is given by a convolutional neural network taking the reduced feature representation as input. The evaluation results showed that the proposed framework outperformed some state-of-the-art approaches in a large margin on both tasks of miRNA-disease associations prediction and miRNA-phenotype associations prediction.\n\nAvailabilityThe software will be available after the manuscript is accepted.\n\nContactjiajiepeng@nwpu.edu.cn;zywei@fudan.edu.cn;shang@nwpu.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.