基于深度学习的miRNA与疾病相关性预测算法

大量研究表明，microRNA（miRNA）在人类复杂疾病研究中发挥着重要作用.识别miRNA与疾病之间的关系对于提高复杂疾病的治疗水平具有重要意义.然而，传统实验方式常受限于小规模和高成本，因此迫切需要计算模拟的方式快速有效地预测miRNA-疾病间的潜在关系.本文通过结合深度学习的堆叠自动编码器算法与旋转森林分类器对miRNA-疾病间关系进行预测.该方法能够有效抽取出融合了疾病语义相似性、miRNA功能相似性和miRNA序列信息的高级特征并对其进行准确分类.在交叉验证实验中，该方法在HMDD v3.0数据集上取得90.30%的预测准确率.此外，我们还在人类复杂疾病乳腺肿瘤上做了案例研究.结果，模型预测得分最高的前30个疾病关联miRNA中28个得到了证实.这些优异的结果表明，该算法是一种有效预测miRNA-疾病关系的工具，能够为生物实验提供高可靠的疾病关联miRNA候选物.

Prediction Algorithm of Association Between miRNAs and Diseases Based on Deep Learning

Numerous studies have shown that microRNA (miRNA) plays important role in the study of human complex diseases.Identifying the association between miRNAs and diseases is important for improving the therapeutic level of complex diseases.However,traditional experimental is often limited to small-scale and high-cost,so computational simulation is urgently needed to quickly and effectively predict the potential miRNAs-disease associations.In this study,a new method is proposed to predict the miRNA-disease association by combining deep learning stacked automatic encoder algorithm with rotation forest classifier.This method can effectively extract high-level features that combine disease semantic similarity,miNRA functional similarity and miRNA sequence information,and accurately classify them.In the cross-validation experiment,this method achieved 90.30% prediction accuracy on the HMDD v3.0 dataset.Furthermore,we have also done case studies on Breast Neoplasms.As a result,28 of the top 30 miRNA-disease associations were confirmed.These excellent results indicate that this method is an effective tool for predicting miRNA-disease associations,and can provide highly reliable candidate miRNAs for biological experiments.