College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
School of Information Science and Engineering, Yunnan University, Kunming 650500, China.
Neural Netw. 2023 Aug;165:491-505. doi: 10.1016/j.neunet.2023.05.052. Epub 2023 Jun 5.
MicroRNAs (miRNA) play critical roles in diverse biological processes of diseases. Inferring potential disease-miRNA associations enable us to better understand the development and diagnosis of complex human diseases via computational algorithms. The work presents a variational gated autoencoder-based feature extraction model to extract complex contextual features for inferring potential disease-miRNA associations. Specifically, our model fuses three different similarities of miRNAs into a comprehensive miRNA network and then combines two various similarities of diseases into a comprehensive disease network, respectively. Then, a novel graph autoencoder is designed to extract multilevel representations based on variational gate mechanisms from heterogeneous networks of miRNAs and diseases. Finally, a gate-based association predictor is devised to combine multiscale representations of miRNAs and diseases via a novel contrastive cross-entropy function, and then infer disease-miRNA associations. Experimental results indicate that our proposed model achieves remarkable association prediction performance, proving the efficacy of the variational gate mechanism and contrastive cross-entropy loss for inferring disease-miRNA associations.
MicroRNAs (miRNA) 在疾病的多种生物学过程中发挥着关键作用。通过计算算法推断潜在的疾病-miRNA 关联，使我们能够更好地理解复杂人类疾病的发生和诊断。本工作提出了一种基于变分门控自动编码器的特征提取模型，用于提取复杂的上下文特征，以推断潜在的疾病-miRNA 关联。具体来说，我们的模型将三种不同的 miRNA 相似度融合到一个综合的 miRNA 网络中，然后将两种不同的疾病相似度分别合并到一个综合的疾病网络中。然后，设计了一种新的图自动编码器，基于变分门机制从 miRNA 和疾病的异构网络中提取多层次表示。最后，通过一种新的对比交叉熵函数，基于门的关联预测器将 miRNA 和疾病的多尺度表示进行组合，并推断疾病-miRNA 关联。实验结果表明，我们提出的模型在关联预测性能方面取得了显著的效果，证明了变分门机制和对比交叉熵损失在推断疾病-miRNA 关联方面的有效性。
