Gene Selection Integrated with Biological Knowledge for Plant Stress Response Using Neighborhood System and Rough Set Theory
Gene Selection Integrated with Biological Knowledge for Plant Stress Response Using Neighborhood System and Rough Set Theory
复制标题
利用邻域系统和粗糙集理论将基因选择与植物逆境响应的生物学知识相结合
DOI：
10.1109/tcbb.2014.2361329
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发表时间：
2015
影响因子：
4.5
通讯作者：
Luan Yushi
中科院分区：
文献类型：
--
作者：
Meng Jun;Zhang Jing;Luan Yushi
从基因表达数据中挖掘知识是生物信息学的研究热点和方向。由于基因表达数据中的基因数量大、样本量小，基因选择和样本分类是一个重要的研究方向。粗糙集理论能够在无冗余的情况下选择属性，已成功地应用于基因选择。为了提高所选基因的可解释性，一些研究人员引入了生物学知识。本文首先利用邻域系统直接处理基因表达数据与生物知识相结合形成的新的信息表，该信息表可以同时呈现多个角度的信息，并且不削弱单个基因的信息，用于选择和分类。然后，利用粗糙集理论中的约简算法，给出了一种新的基因选择框架，并基于该框架提出了一种有意义的基因选择方法。将该方法应用于植物胁迫响应分析。在三个数据集上的实验结果表明，该方法是有效的，它可以在没有冗余的情况下选择有意义的基因子集，并获得较高的分类精度。对结果的生物学分析表明，该方法具有良好的可解释性。
Mining knowledge from gene expression data is a hot research topic and direction of bioinformatics. Gene selection and sample classification are significant research trends, due to the large amount of genes and small size of samples in gene expression data. Rough set theory has been successfully applied to gene selection, as it can select attributes without redundancy. To improve the interpretability of the selected genes, some researchers introduced biological knowledge. In this paper, we first employ neighborhood system to deal directly with the new information table formed by integrating gene expression data with biological knowledge, which can simultaneously present the information in multiple perspectives and do not weaken the information of individual gene for selection and classification. Then, we give a novel framework for gene selection and propose a significant gene selection method based on this framework by employing reduction algorithm in rough set theory. The proposed method is applied to the analysis of plant stress response. Experimental results on three data sets show that the proposed method is effective, as it can select significant gene subsets without redundancy and achieve high classification accuracy. Biological analysis for the results shows that the interpretability is well.