School of Computer and Information Science, Southwest University, Chongqing 400715, China.
Biomed Res Int. 2018 Nov 11;2018:2862458. doi: 10.1155/2018/2862458. eCollection 2018.
Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work.
质谱（MS）是蛋白质研究中的一项重要技术。通过 MS 数据进行有效的分类方法可以有助于早期、非侵入性的诊断，也有助于生物信息学领域的发展。由于 MS 数据具有高维性，因此已经广泛研究了能够有效处理大量 MS 数据的适当方法。在本文中，研究了基于智能算法的方法的应用。首先，讨论了使用典型机器学习方法的分类和生物标志物分析方法。然后是集成策略算法。显然，简单和基本的机器学习算法几乎无法满足蛋白质 MS 分类的各种需求。还研究了预处理算法，因为这些方法对于特征选择或特征提取以提高分类性能很有用。随着数据量的增加，蛋白质 MS 数据变得越来越复杂和庞大；在进一步的工作中，更加强调分类器选择以及不同算法和预处理算法的组合方面的分类方法的改进。
