Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA–miRNA associations

作者：Lu-Xiang Guo, Lei Wang, Zhu‐Hong You, Chang-Qing Yu, Menglei Hu, Bo-Wei Zhao, Yang Li · 发表于：Briefings in Bioinformatics · 年份：2024 · DOI：10.1093/bib/bbae020 · 被引用次数：37 · 研究领域：Circular RNAs in diseases、Cancer-related molecular mechanisms research、MicroRNA in disease regulation

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance com...