Li Xin, Ma Li, Wang Jinjia, Zhao Chun
Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2011 Apr;28(2):410-4.
Feature selection (FS) techniques have become an important tool in bioinformatics field. The core algorithm of it is to select the hidden significant data with low-dimension from high-dimensional data space, and thus to analyse the basic built-in rule of the data. The data of bioinformatics fields are always with high-dimension and small samples, so the research of FS algorithm in the bioinformatics fields has great foreground. In this article, we make the interested reader aware of the possibilities of feature selection, provide basic properties of feature selection techniques, and discuss their uses in the sequence analysis, microarray analysis, mass spectra analysis etc. Finally, the current problems and the prospects of feature selection algorithm in the application of bioinformatics is also discussed.
特征选择（FS）技术已成为生物信息学领域的一种重要工具。其核心算法是从高维数据空间中选择低维的隐藏重要数据，从而分析数据的基本内在规律。生物信息学领域的数据总是具有高维和小样本的特点，因此FS算法在生物信息学领域的研究具有广阔前景。在本文中，我们让感兴趣的读者了解特征选择的可能性，提供特征选择技术的基本属性，并讨论它们在序列分析、微阵列分析、质谱分析等方面的应用。最后，还讨论了特征选择算法在生物信息学应用中的当前问题和前景。
