在糖蛋白组学中核糖的识别:一种ML方法,用于在质谱学中解决糖迁移问题
Yuanjie Su1, Chang Jiang1, Ziyue Yang1
1School of Computer Science and Technology, Xidian University, Xi'an, 710071, China.
Bioinformatics advances
|September 22, 2025
概括
现在可以使用机器学习模型来准确识别核心的fucosylated glycopeptides,这些模型可以在质谱学中克服fucose迁移的挑战. 这些模型实现了高精度,使生物样本中核核糖可靠分析.
科学领域:
- 葡萄糖化学物质和质谱学
- 计算生物学和生物信息学
背景情况:
- 核化是一种关键的翻译后修改,影响生物功能.
- 鉴定核心的fucosylated glycopeptides是困难的,因为在质谱测量期间的fucose迁移.
研究的目的:
- 开发可靠的计算模型,以准确识别核心的基化糖.
- 为了应对质谱数据分析中福克斯迁移的挑战.
主要方法:
- 利用FUT8淘汰赛和正常小鼠大脑中的糖,用于模型训练和验证.
- 开发了半监督和自我监督的机器学习模型,使用特征性离子强度作为特征向量.
- 在独立数据集上评估模型性能和稳定性.
主要成果:
- 通过对小鼠大脑数据的半监督模型,实现了99.95%的核心基基的识别准确度.
- 成功地将模型应用于野生型老鼠大脑,人类IgG和人类血清,证实了可靠的核心糖丰度.
- 证明了机器学习在克服fucose迁移文物方面的有效性.
结论:
- 开发的模型提供了一种可靠和准确的方法,用于从高通量质谱数据中识别核心核糖.
- 强调数据质量,特征工程和机器学习对于稳健的糖分析的重要性.
- 计算工具是公开可用的,以促进进一步的研究在糖科学.
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