Liquid Chromatography coupled to Tandem Mass Spectrometry (LC‐MS/MS) based methods are currently the top choice for high‐throughput, quantitative measurements of the proteome. While traditional proteomics LC‐MS/MS methods can suffer from issues such as low reproducibility and quantitative accuracy due to its stochastic nature, recent improvements in acquisition protocols have resulted in methods that can overcome these challenges. Data‐independent acquisition (DIA) is a novel mass spectrometric method that does so by using a deterministic acquisition strategy. These new approaches will allow researchers to apply MS on more complex samples, however, existing heuristic and expert‐knowledge based methods will struggle with keeping pace of the increasing complexity of the resulting data. Deep learning (DL) based methods have been shown to be more adept at handling large amounts of complex data than traditional methods in many other fields, such as computer vision and natural language processing. Proteomics is also entering a phase where the size and complexity of the data will require us to look towards scalable and data‐driven DL pipelines.

中文翻译：

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DIA 数据质谱分析中的机器学习。

液相色谱与串联质谱 (LC-MS/MS) 相结合的方法目前是蛋白质组高通量定量测量的首选。虽然传统的蛋白质组学 LC-MS/MS 方法由于其随机性而存在重现性和定量准确性低等问题，但最近采集协议的改进已经产生了可以克服这些挑战的方法。数据独立采集 (DIA) 是一种新颖的质谱方法，它通过使用确定性采集策略来实现。这些新方法将允许研究人员将 MS 应用于更复杂的样本，但是，现有的启发式和基于专家知识的方法将难以跟上结果数据日益复杂的步伐。在许多其他领域，例如计算机视觉和自然语言处理，基于深度学习 (DL) 的方法已被证明比传统方法更擅长处理大量复杂数据。蛋白质组学也正在进入一个阶段，数据的大小和复杂性将要求我们寻找可扩展和数据驱动的 DL 管道。