MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.
微RNA（miRNA）是一类通过不完全碱基互补配对实现后转录调控作用的小分子非编码RNA，其往往在癌症患者的病灶和外周血中表达失调。近年来，基于人工智能算法如机器学习和深度学习的模型逐渐应用于miRNA生物信息学研究。与传统的生物信息学工具比较，基于人工智能算法的miRNA靶点预测工具准确度更高，并实现了miRNA亚细胞定位和亚细胞重分布的预测，进一步深化了科研人员对miRNA的认识。此外，人工智能算法在临床模型构建的应用也显著提升了miRNA生物标志物的挖掘效率。本文总结了近年来人工智能算法在miRNA靶点预测、亚细胞定位和生物标志物挖掘的应用，并探讨了机器学习和深度学习对癌症相关miRNA研究的潜在价值。.
Keywords: Clinical prediction model; Deep learning; Machine learning; MicroRNA; Review; Subcellular distribution; Target prediction.