Top Scoring Pair Decision Tree for Gene Expression Data Analysis
Top Scoring Pair Decision Tree for Gene Expression Data Analysis
复制标题
DOI：
10.1007/978-1-4419-7046-6_3
复制
发表时间：
2011-01-01
期刊：
影响因子：
--
通讯作者：
Kretowski, Marek
中科院分区：
文献类型：
--
作者：
Czajkowski, Marcin;Kretowski, Marek
微阵列数据的分类问题可以通过人类专家易于理解和解释的方法成功地执行，例如决策树或最高评分对算法。在本章中，我们提出了一种结合上述方法的混合解决方案。所提出的决策树的应用，根据基因表达值的成对比较来分割实例，可能对于基因组研究和基础过程的科学建模具有巨大的潜力。我们在 11 个公共领域微阵列数据集上将提出的解决方案与 TSP 系列方法和决策树进行了比较，结果令人鼓舞。
Classification problems of microarray data may be successfully performed with approaches by human experts which are easy to understand and interpret, like decision trees or Top Scoring Pairs algorithms. In this chapter, we propose a hybrid solution that combines the above-mentioned methods. An application of presented decision trees, which splits instances based on pairwise comparisons of the gene expression values, may have considerable potential for genomic research and scientific modeling of underlying processes. We have compared proposed solution with the TSP-family methods and decision trees on 11 public domain microarray datasets and the results are promising.