Feature Selection and Machine Learning with Mass Spectrometry Data
Feature Selection and Machine Learning with Mass Spectrometry Data
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DOI：
10.1007/978-1-60327-194-3_11
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发表时间：
2010-01-01
期刊：
影响因子：
--
通讯作者：
Pihur, Vasyl
中科院分区：
文献类型：
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作者：
Datta, Susmita;Pihur, Vasyl
质谱法在生物化学研究中的应用由来已久。然而，在过去的几年里，它的潜力，发现蛋白质组生物标志物，利用蛋白质质谱引起了极大的兴趣。尽管其潜在的生物标志物的发现，它是公认的，有意义的蛋白质组学特征的质谱鉴定需要仔细评估。因此，提取有意义的特征和区分油这些特征的基础上的样本仍然是开放的研究领域。几个研究小组积极参与使这一过程尽可能完美。在这一章中，我们提供了一个主要的贡献，对功能选择和分类的蛋白质组质谱涉及MALDI-TOF和SELDI-TOF技术。
Mass spectrometry has been used in biochemical research for a long time. However, its potential for discovering proteomic biomarkers using protein mass spectra has aroused tremendous interest in the last few years. In spite of its potential for biomarker discovery, it is recognized that the identification of meaningful proteomic features from mass spectra needs careful evaluation. Hence, extracting meaningful features and discriminating the samples based oil these features are still open areas of research. Several research groups are actively involved in making the process as perfect as possible. In this chapter, we provide a review of major contributions toward feature selection and classification of proteomic mass spectra involving MALDI-TOF and SELDI-TOF technology.