Advance in applications of artificial intelligence algorithms in cancer-related miRNA research.

Q2 Medicine
Hongyu Lu, Jia Zhang, Yixing Cao, Shuming Wu, Xingyan Wang, Yurong Bai, Chang Zhao, Jun Zhu, Yuan Wei, Runting Yin
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引用次数: 0

Abstract

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. Bioinformatic tools could improve efficiency of miRNA research, while current bioinformatic tools are in lack of sufficient accuracy. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in the bioinformatical tools. MiRNA target prediction tools based on artificial intelligence algorithms have higher accuracy than traditional target prediction tools. Bioinformatic tools based on artificial intelligence algorithms successfully predicted miRNA subcellular localization and redistribution, which advanced researchers' understanding of miRNAs. Additionally, the clinical application of artificial intelligence algorithms improved the development of miRNA biomarkers. In this article, we summarized recently developed miRNA bioinformatic tools based on artificial intelligence algorithms. And the potential of machine learning and deep learning in the miRNA research was also highlighted.
人工智能算法在癌症相关 miRNA 研究中的应用进展。
MiRNA 是一类小型非编码 RNA,通过部分互补碱基配对在转录后调节基因表达。据报道,肿瘤组织和癌症患者外周血中的 miRNA 表达存在异常。生物信息工具可以提高 miRNA 研究的效率,但目前的生物信息工具缺乏足够的准确性。近年来,机器学习和深度学习等人工智能算法在生物信息工具中得到了广泛应用。与传统的靶标预测工具相比,基于人工智能算法的 MiRNA 靶标预测工具具有更高的准确性。基于人工智能算法的生物信息学工具成功预测了 miRNA 亚细胞定位和再分布,促进了研究人员对 miRNA 的了解。此外,人工智能算法的临床应用也促进了 miRNA 生物标志物的开发。本文总结了最近开发的基于人工智能算法的 miRNA 生物信息学工具。文章还强调了机器学习和深度学习在 miRNA 研究中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.80
自引率
0.00%
发文量
67
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