Here, the authors reason that the complexity of medical problems and proteome science might be tackled effectively with deep learning (DL) technology. However, deployment of DL for proteomics data requires the acquisition of data sets from a large number of samples. Based on the success of DL in medical imaging classification, proteome data from thousands of samples are arguably the minimal input for DL. Contemporary proteomics is turning high‐throughput thanks to the rapid progresses of sample preparation and liquid chromatography mass spectrometry methods. In particular, data‐independent acquisition now enables the generation of hundreds to thousands of quantitative proteome maps from clinical specimens in clinical cohorts with only limited sample amounts in clinical cohorts. Upheavals in the design of large‐scale clinical proteomics studies might be required to generate proteomic big data and deploy DL to tackle complex medical problems.

中文翻译：

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为精准医学生成蛋白质组学大数据。

在这里，作者认为可以通过深度学习 (DL) 技术有效地解决医学问题和蛋白质组科学的复杂性。但是，针对蛋白质组学数据部署 DL 需要从大量样本中获取数据集。基于深度学习在医学影像分类方面的成功，来自数千个样本的蛋白质组数据可以说是深度学习的最小输入。由于样品制备和液相色谱质谱方法的快速发展，当代蛋白质组学正在变得高通量。特别是，数据独立采集现在可以从临床队列中的临床标本中生成数百到数千个定量蛋白质组图，而临床队列中的样本量有限。