Identification of disease-related
microRNAs (disease
miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease
miRNAs by integrating the similarities and associations of
miRNAs and diseases. However, these methods fail to learn the deep features of the
miRNA similarities, the disease similarities, and the miRNA⁻disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease
miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of
miRNAs and diseases, but also captures the topology structures of the
miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA⁻disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of
miRNAs and diseases. The novel
miRNA and disease similarities which contain the topology structures are obtained by random walks on the
miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on
breast cancer,
colorectal cancer and
lung cancer further demonstrate CNNDMP's powerful ability of discovering potential disease
miRNAs.