Shen Qi, Shi Wei-min, Kong Wei
Chemistry Department, Zhengzhou University, Zhengzhou 450052, China.
J Biomed Inform. 2009 Feb;42(1):59-65. doi: 10.1016/j.jbi.2008.05.011. Epub 2008 Jun 17.
In the analysis of gene expression profiles, the selection of genetic markers and precise diagnosis of cancer type are crucial for successful treatment. The selection of discriminatory genes is critical to improve the accuracy and decrease computational complexity and cost in microarray analysis. In this paper, we developed a new statistical parameter, the suitability score to filter genes which only utilize sample distances from the class centroid. The filtered genes are employed in the nearest centroid classification to classify cancer. To evaluate the performance of the new statistical parameter, the proposed approach is applied to three publicly available microarray datasets. In this paper we demonstrate that the proposed gene selection method is steady in handling classification tasks and is a useful tool for gene selection and mining high dimension data.
在基因表达谱分析中，遗传标记的选择和癌症类型的精确诊断对于成功治疗至关重要。鉴别基因的选择对于提高微阵列分析的准确性、降低计算复杂性和成本至关重要。在本文中，我们开发了一种新的统计参数——适用性得分，用于筛选仅利用样本到类质心距离的基因。筛选出的基因用于最近质心分类以对癌症进行分类。为了评估新统计参数的性能，将所提出的方法应用于三个公开可用的微阵列数据集。在本文中，我们证明了所提出的基因选择方法在处理分类任务时是稳定的，并且是基因选择和挖掘高维数据的有用工具。
