Reduct Generation and Classification of Gene Expression Data
Reduct Generation and Classification of Gene Expression Data
复制标题
减少基因表达数据的生成和分类
DOI：
10.1109/ichit.2006.203
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Rana Datta Gupta
中科院分区：
文献类型：
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作者：
Bashirahamad Momin;Sushmita Mitra;Rana Datta Gupta
识别负责从现有基因芯片数据样本中区分的基因亚集是生物信息学中的一个重要任务。由于样本中含有大量的基因，存在着指数级大的解的搜索空间。主要的挑战是减少或移除多余的基因，而不影响物体之间的辨别能力。粗糙集理论中的约简对应于基本基因的一个最小子集。我们提出了一种从基因微阵列数据生成约简的算法。它通过对基因表达数据进行预处理，将实值属性离散化为范畴，然后使用基于正区域的方法生成约简。为了进行比较，还讨论了不同的还原生成方法。在基准基因表达数据集上的结果表明，冗余基因减少了90%以上
Identification of gene subsets responsible for discerning between available samples of gene microarray data is an important task in bioinformatics. Due to the large number of genes in samples, there is an exponentially large search space of solutions. The main challenge is to reduce or remove the redundant genes, without affecting discernibility between objects. Reducts, from rough set theory, correspond to a minimal subset of essential genes. We present an algorithm for generating reducts from gene microarray data. It proceeds by preprocessing gene expression data, discretization of real value attributes into categorical followed by positive region based approach for reduct generation. For comparison, different approaches for reduct generation have also been discussed. Results on benchmark gene expression datasets demonstrate more than 90% reduction of redundant genes