DbyDeep:通过深度学习探索MS可检测
Juho Son1, Seungjin Na1,2, Eunok Paek1,2
1Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea.
Analytical chemistry
|July 17, 2023
概括
在质谱法 (MS) 中检测对蛋白质组学至关重要. 一个新的深度学习模型,DbyDeep,通过考虑不仅仅是序列,提高了的检测性预测.
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
- 质谱测量质量谱测量
背景情况:
- 对于基于质谱 (MS) 的蛋白质组学,特别是针对性蛋白质组学,的检测性预测至关重要.
- 当前的机器学习方法通常仅依赖于序列或物理化学性质.
- 的检测受到测序之外的实验因素的影响,包括样品准备,消化,分离,电离和MS参数.
研究的目的:
- 开发一种改进的计算模型,用于预测质谱学中的检测能力.
- 将超越序的上下文信息纳入预测模型.
- 与现有方法相比,评估新模型的性能.
主要方法:
- 开发了DbyDeep,这是一个端到端的长短期存储器 (LSTM) 网络.
- 将序列上下文和蛋白酶分裂部位上下文纳入深度学习模型.
- 在多个MS/MS数据集上评估了DbyDeep,跨越多种物种和MS仪器.
主要成果:
- 与现有方法相比,DbyDeep在预测的检测性方面表现优异.
- 包括分离点上下文在内显著提高了预测准确度.
- 该模型成功地预测了来自不同数据集的,表明了强度.
结论:
- 为了准确预测的可检测性,需要一个包含多个上下文因素的全面学习模型.
- DbyDeep 通过考虑序列和裂痕部位的背景,提供了更有效的方法.
- 这些发现强调了实验背景在基于MS的蛋白质组学分析中的重要性.
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