Artificial intelligence for proteomics and biomarker discovery
Artificial intelligence for proteomics and biomarker discovery
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用于蛋白质组学与生物标志物发现的人工智能技术
DOI：
10.1016/j.cels.2021.06.006
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发表时间：
2021-08-18
期刊：
影响因子：
9.3
通讯作者：
Strauss, Maximilian T.
中科院分区：
文献类型：
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作者：
Mann, Matthias;Kumar, Chanchal;Strauss, Maximilian T.
生物医学数据如雪崩般产生，分析和理解这些数据的计算能力也在并行扩展。从基因组测序和广泛应用的深度测序技术开始，这些趋势现在已经在所有组学学科中占据了主导地位，并且越来越需要多组学集成以及人工智能技术的数据解释。在这里，我们专注于基于质谱（MS）的蛋白质组学，并描述机器学习，特别是深度学习现在如何仅从氨基酸序列预测实验性肽测量。这将极大地提高分析工作流程的质量和可靠性，因为实验结果应该与多维数据环境中的预测一致。机器学习也成为从蛋白质组学数据中发现生物标志物的核心，现在开始超越现有的同类最佳分析。最后，我们讨论了在临床环境中部署基于ms的生物标志物所需的模型透明度和可解释性以及数据隐私。
There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.