[深度学习应用在质谱成像数据分析中的研究进展]
Dong-Dong Huang1,2, Xin-Yu Liu1, Guo-Wang Xu1,2
1CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China.
Se pu = Chinese journal of chromatography
|July 5, 2024
概括
深度学习 (DL) 通过解决数据复杂性和提高图像质量来增强质谱成像 (MSI) 分析. 本综述探讨了DL在MSI预处理,重建和分析中的应用,用于诸如瘤诊断等领域.
科学领域:
- 分析化学 分析化学
- 计算生物学 计算生物学
- 医疗成像医学成像
背景情况:
- 质谱成像 (MSI) 产生复杂的,高维的数据.
- 随着数据量和复杂性的增加,后期处理面临重大挑战,包括噪音和注册错误.
- 深度学习 (DL) 为自动化数据分析和特征提取提供了先进的功能.
研究的目的:
- 审查深度学习应用在质谱成像数据分析中的当前状态.
- 突出DL在克服MSI数据挑战方面的潜力.
- 讨论结合AI和MSI技术的未来趋势.
主要方法:
- 对MSI中DL应用的现有文献的审查.
- 专注于四个关键阶段:数据预处理,图像重建,集群分析和多式联络融合.
- 在瘤诊断和亚型分类中DL-MSI的插图.
主要成果:
- DL有效地解决了MSI数据中的噪音,背景干扰和注册偏差.
- 通过转移学习等技术,DL可实现自动化特征提取和深入分析.
- 与MSI结合的DL在医疗应用中表现有前途,特别是在瘤学中.
结论:
- 深度学习提供了一种强大的方法来增强质谱成像数据分析.
- 对于推进MSI应用程序,特别是复杂的生物和医学研究中,DL集成至关重要.
- 未来的研究应该专注于进一步整合人工智能和MSI,以获得更好的见解和应用.
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