基于基因表达谱的肿瘤样本分类规则提取

样本分类规则提取是基因表达谱数据挖掘工作中的重要内容,提取肿瘤病理组织与正常组织的样本分类规则具有重要的生物学意义与临床诊断价值.针对该问题,基于机器学习与数据挖掘技术,研究了用于区分肿瘤与正常组织样本的分类规则提取问题.首先,利用改进的Relief算法生成候选特征子集,并以支持向量机作为样本分类模型,利用交叉验证方法在训练集上评估候选特征子集的样本分类能力,确定分类特征基因集合;然后,利用CART(classification and regression trees)学习算法构建决策树获得样本分类规则;最后,对所得规则进行了分析和解释.

Rule extraction for tumor/normal tissue classification based on microarray data

Classification rule extraction is an important technique for acquiring knowledge from data in the fields of machine learning and data mining.DNA microarray technology can monitor the expression patterns of thousands of genes simultaneously in a single experiment,and thus provides a successful way to a comprehensive understanding of the genetic alterations presented in tumors.Extracting rules from microarray data for distinguishing tumor tissue samples from normal ones can provide useful information to under...