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预测小分子-miRNA关联的机器学习方法:一个全面的审查
Ashish Panghalia1, Vikram Singh2
1Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Kangra, 176215, India.
Molecular diversity
|May 20, 2025
概括
机器学习 (ML) 加快了小分子-微RNA关联 (SMA) 的发现,这对于疾病诊断和治疗至关重要. 本综述详细介绍了ML方法,数据资源和预测这些重要相互作用的趋势.
科学领域:
- 计算生物学和生物信息学
- 药物发现和开发 药物发现和开发
背景情况:
- 微RNAs (miRNAs) 是关键的调节者,与人类疾病有关.
- 小分子 (SMs) 可以调节miRNA活动,提供治疗潜力.
- 由于miRNA的结构复杂性,对SM-miRNA关联 (SMA) 的实验性确定具有挑战性.
研究的目的:
- 审查用于预测SMA的机器学习 (ML) 方法.
- 为数据资源,特征提取和ML算法提供全面的概述.
- 分析现有的基于ML的SMA预测方法的趋势和性能.
主要方法:
- 汇编和分析了32种基于ML的SMA预测方法.
- 将方法分为六类:经典的ML,深度学习,矩阵分解,网络传播,图形学习和集体学习.
- 对ML算法的数据资源,特征工程技术和性能指标的审查.
主要成果:
- 32种基于ML的SMA预测方法的详细普查和分类.
- 分析不同ML方法中的算法使用和性能趋势.
- 确定关键原则和最先进方法的比较性能.
结论:
- 机器学习为SMAs的实验查提供了一个具有成本效益和高效的替代方案.
- 本综述为SMA预测的当前ML方法提供了关键的见解.
- 突出了未来研究的关键领域,以推进针对miRNA的ML驱动药物发现.
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