Center of Intelligent Computing and Applied Statistics, School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China.
Molecules. 2023 Jun 27;28(13):5013. doi: 10.3390/molecules28135013.
Numerous pieces of evidence have indicated that microRNA (miRNA) plays a crucial role in a series of significant biological processes and is closely related to complex disease. However, the traditional biological experimental methods used to verify disease-related miRNAs are inefficient and expensive. Thus, it is necessary to design some excellent approaches to improve efficiency. In this work, a novel method (CFSAEMDA) is proposed for the prediction of unknown miRNA-disease associations (MDAs). Specifically, we first capture the interactive features of miRNA and disease by integrating multi-source information. Then, the stacked autoencoder is applied for obtaining the underlying feature representation. Finally, the modified cascade forest model is employed to complete the final prediction. The experimental results present that the AUC value obtained by our method is 97.67%. The performance of CFSAEMDA is superior to several of the latest methods. In addition, case studies conducted on lung neoplasms, breast neoplasms and hepatocellular carcinoma further show that the CFSAEMDA method may be regarded as a utility approach to infer unknown disease-miRNA relationships.
大量证据表明，microRNA（miRNA）在一系列重要的生物学过程中发挥着关键作用，与复杂疾病密切相关。然而，用于验证与疾病相关的 miRNA 的传统生物学实验方法效率低下且昂贵。因此，有必要设计一些优秀的方法来提高效率。在这项工作中，提出了一种新的方法（CFSAEMDA）用于预测未知的 miRNA-疾病关联（MDA）。具体来说，我们首先通过整合多源信息来捕获 miRNA 和疾病之间的交互特征。然后，应用堆叠自动编码器来获得潜在的特征表示。最后，采用改进的级联森林模型完成最终预测。实验结果表明，我们的方法获得的 AUC 值为 97.67%。CFSAEMDA 的性能优于几种最新方法。此外，对肺癌、乳腺癌和肝细胞癌的案例研究进一步表明，CFSAEMDA 方法可被视为推断未知疾病-miRNA 关系的实用方法。
