Abstract
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.
Highlights
Introduction miRNAs are non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. miRNAs exert their biological functions primarily via regulating the expression of target genes. miRNAs usually target to a specific sequence in the 3 untranslated terminal of mRNAs, inhibiting the translation of the target genes [1,2,3,4,5]
Several computational methods have been proposed for predicting disease-associated miRNAs, which can be classified into two main categories in general. miRNAs implement their biological functions by regulating the expression of their target mRNAs [9]
Liu et al [21] and Liao et al [22] proposed the method of predicting miRNA–disease associations via random walking in networks composed of multiple data sources
Summary
MiRNAs are non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. miRNAs exert their biological functions primarily via regulating the expression of target genes (mRNAs). miRNAs usually target to a specific sequence in the 3 untranslated terminal of mRNAs, inhibiting the translation of the target genes [1,2,3,4,5]. MiRNAs exert their biological functions primarily via regulating the expression of target genes (mRNAs). The methods in the second category are based on the biological observation that miRNAs with similar functions are usually associated with similar diseases and vice versa [15,16,17,18]. Ding et al [26] integrated known miRNA–disease associations and experimentally validated miRNA–target associations and proposed a prediction method based on a disease–miRNA–target heterogeneous network. As these methods are based on the traditional computing model [27,28,29], it is difficult to extract the deep feature representation from the multiple kinds of data
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