Kelchtermans Pieter, Bittremieux Wout, De Grave Kurt, Degroeve Sven, Ramon Jan, Laukens Kris, Valkenborg Dirk, Barsnes Harald, Martens Lennart
Department of Medical Protein Research, VIB, Ghent, Belgium; Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium; Flemish Institute for Technological Research (VITO), Boeretang, Mol, Belgium.
Proteomics. 2014 Mar;14(4-5):353-66. doi: 10.1002/pmic.201300289. Epub 2014 Jan 21.
Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.
机器学习是人工智能的一个子领域，专注于使计算机能够从现有数据中学习解决（复杂）问题的算法。如果有足够的数据来训练并随后评估算法，这种能力可用于生成针对特别棘手问题的解决方案。由于基于质谱的蛋白质组学不乏复杂问题，且公开可用的数据量也在不断增加，机器学习正迅速成为该领域非常受欢迎的工具。因此，我们在此概述机器学习在蛋白质组学中的不同应用，这些应用共同涵盖了几乎整个湿实验室和干实验室工作流程，并解决了实验规划与设计以及数据处理与分析中的关键瓶颈问题。
