The important role of microRNA (miRNA) in human diseases has been confirmed by some studies. However, only using biological experiments has greater blindness, leading to higher experimental costs. In this paper a high-efficiency algorithm based on a variety of biological source information and applying a combination of a convolutional neural network (CNN) feature extractor and an extreme learning machine (ELM) classifier is proposed. Specifically, the semantic similarity of diseases, the gaussian interaction profile kernel similarity of the four biological information of miRNA, disease, long non-coding RNA (lncRNA) and environmental factors (EFs), and the similarities of miRNAs are fused together. Among them, miRNAs similarity is composed of miRNA target information, sequence information, family information, and function information. Then, the dimensionality of the data set is reduced by the autoencoder (AE). Finally, deep features are extracted through CNN, and then the association between miRNA and disease is predicted by ELM. The experimental results show that the average AUC value based on the multi-biological source information (MSCNE) model is 0.9630, which can reach higher performance than the other classic classifier, feature extractor mentioned and the other existing algorithms. The results show the MSCNE algorithm is effective to predict the correlation of miRNA-disease.

中文翻译：

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MSCNE：使用基于多源生物信息的神经网络预测 miRNA-疾病关联

microRNA（miRNA）在人类疾病中的重要作用已被一些研究证实。然而，仅使用生物实验具有更大的盲目性，导致更高的实验成本。本文提出了一种基于多种生物源信息，结合卷积神经网络（CNN）特征提取器和极限学习机（ELM）分类器的高效算法。具体而言，将疾病的语义相似性、miRNA、疾病、长链非编码RNA（lncRNA）和环境因素（EFs）四种生物学信息的高斯交互谱核相似性以及miRNA的相似性融合在一起。其中，miRNAs的相似性由miRNA靶点信息、序列信息、家族信息和功能信息组成。然后，自动编码器（AE）降低了数据集的维数。最后通过 CNN 提取深层特征，然后通过 ELM 预测 miRNA 与疾病之间的关联。实验结果表明，基于多生物源信息（MSCNE）模型的平均AUC值为0.9630，可以达到比其他经典分类器、特征提取器和其他现有算法更高的性能。结果表明MSCNE算法在预测miRNA-疾病相关性方面是有效的。实验结果表明，基于多生物源信息（MSCNE）模型的平均AUC值为0.9630，可以达到比其他经典分类器、特征提取器和其他现有算法更高的性能。结果表明MSCNE算法在预测miRNA-疾病相关性方面是有效的。实验结果表明，基于多生物源信息（MSCNE）模型的平均AUC值为0.9630，可以达到比其他经典分类器、特征提取器和其他现有算法更高的性能。结果表明MSCNE算法在预测miRNA-疾病相关性方面是有效的。