Abstract：
Since scientific investigations have demonstrated that aberrant expression of miRNAs brings about the incidence of numerous intricate diseases, precise determination of miRNA-disease relationships greatly contributes to the advancement of human medical progress. To tackle the issue of inefficient conventional experimental approaches, numerous computational methods have been proposed to predict miRNA-disease association with enhanced accuracy. However, constructing miRNA-gene-disease heterogeneous network by incorporating gene information has been relatively under-explored in existing computational techniques. Accordingly, this paper puts forward a technique to predict miRNA-disease association by applying autoencoder and implementing random walk on miRNA-gene-disease heterogeneous network(AE-RW). Firstly, we integrate association information and similarities between miRNAs, genes, and diseases to construct a miRNA-gene-disease heterogeneous network. Subsequently, we consolidate two network feature representations extracted independently via an autoencoder and a random walk procedure. Finally, deep neural network(DNN) are utilized to conduct association prediction. The experimental results demonstrate that the AE-RW model achieved an AUC of 0.9478 through 5-fold CV on the HMDD v3.2 dataset, outperforming the five most advanced existing models. Additionally, case studies were implemented for breast and lung cancer, further validated the superior predictive capabilities of our model.
摘要：
由于科学研究表明miRNA的异常表达会导致许多复杂疾病的发生，miRNA与疾病关系的精确测定极大地促进了人类医学的进步。为了解决传统实验方法效率低下的问题，已经提出了许多计算方法来预测miRNA-疾病相关具有增强的准确性。然而,通过整合基因信息构建miRNA-基因-疾病异质性网络在现有计算技术中的探索相对不足。因此，本文提出了一种通过自动编码器并在miRNA-基因-疾病异质性网络（AE-RW）上实现随机游走来预测miRNA-疾病关联的技术。首先,我们整合了miRNA之间的关联信息和相似性，基因，构建miRNA-基因-疾病异质性网络。随后，我们合并了通过自动编码器和随机游走过程独立提取的两个网络特征表示。最后,利用深度神经网络(DNN)进行关联预测。实验结果表明，AE-RW模型在HMDDv3.2数据集上通过5倍CV实现了0.9478的AUC，超越了现有的五种最先进的模式。此外,对乳腺癌和肺癌进行了案例研究，进一步验证了我们模型的优越预测能力。
