School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
Department of Medical Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
Zhejiang Da Xue Xue Bao Yi Xue Ban. 2024 Apr 25;53(2):231-243. doi: 10.3724/zdxbyxb-2023-0511.
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.
miRNAs 是一类小的非编码 RNA，通过部分互补碱基配对在转录后调控基因表达。在肿瘤组织和癌症患者的外周血中已经报道了异常的 miRNA 表达。近年来，机器学习和深度学习等人工智能算法已广泛应用于生物信息学研究。与传统的生物信息学工具相比，基于人工智能算法的 miRNA 靶标预测工具具有更高的准确性，并且可以成功预测 miRNA 的亚细胞定位和重新分布，从而加深我们的理解。此外，基于人工智能算法构建临床模型可以显著提高 miRNA 作为生物标志物的挖掘效率。本文总结了基于人工智能算法的生物信息学 miRNA 工具的最新进展，重点介绍了机器学习和深度学习在癌症相关 miRNA 研究中的潜力。
