机器学习在甲状腺疾病中的基于质谱的多组学的应用
Yanan Che1, Meng Zhao1, Yan Gao1
1School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.
Frontiers in molecular biosciences
|January 1, 2025
概括
机器学习 (ML) 结合基于质谱 (MS) 的多组学,为诊断甲状腺疾病提供了一种强大的方法. 本综述探讨了ML在分析复杂的蛋白质组学和代谢组学数据中的应用,以改善甲状腺疾病的检测.
科学领域:
- 生物医学数据分析
- 计算生物学是一种计算生物学.
- 内分泌学 在内分泌学.
背景情况:
- 甲状腺疾病对健康造成重大负担,需要准确和及时的诊断方法.
- 基于质谱学 (MS) 的多组学,特别是蛋白质组学和代谢组学,为疾病机制提供了洞察力.
- 越来越多的生物医学数据需要先进的分析技术.
研究的目的:
- 审查机器学习 (ML) 在甲状腺疾病研究中的基于MS的多组学的应用.
- 探索ML与多omics数据的整合,以改善甲状腺疾病诊断.
主要方法:
- 讨论基于MS的多omics技术,包括蛋白质组学和代谢组学.
- 概述常用的ML算法:无监督 (例如,主要组件分析,层次聚类) 和监督 (例如,随机森林,支持矢量机器).
- 探索ML集成与MS基于多omics数据的ML集成.
主要成果:
- 机器学习技术在分析复杂的基于MS的多omics数据方面是有效的.
- 将ML与蛋白质学和代谢学数据的整合显示出甲状腺疾病诊断的前景.
- 特定的ML算法因其在这个领域的实用性而被突出.
结论:
- 应用于基于MS的多组体的ML代表了促进甲状腺疾病诊断的有希望的策略.
- 结合这些技术的进一步研究可以带来更准确,更有效的诊断工具.
- 这种方法有助于理解甲状腺病理的复杂生物学基础.
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