使用批量效应移除神经网络 (BERNN) 增强液态染色学质谱数据的分类
Arnaud Droit1, Simon Pelletier2, Mickaël Leclerq3
1Centre de Recherche du CHU de Québec - Université Laval, Axe Endocrinologie et Néphrologie, Québec, Canada.
Research square
|July 18, 2023
概括
批量效应移除神经网络 (BERNN) 在大型液态染色体质谱 (LC-MS) 实验中改善样本分类. 伯恩模型有效地对新批次进行概括,同时保持关键的生物变异.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
- 分析化学 分析化学
背景情况:
- 液体染色学质谱 (LC-MS) 对于生物样本分析至关重要.
- 来自实验变化的批量效应阻碍了数据的解释性和可重现性.
- 现有的批量效应校正方法往往会损害真正的生物信号.
结论:
- 伯恩提供了一个有前途的解决方案,用于减轻大规模LC-MS数据中的批量效应.
- 有效消除批量效应与保护生物多样性的平衡至关重要.
- 这项工作通过改进的数据处理技术,推进可重复蛋白质组学研究.
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