Boosting for tumor classification with gene expression data
摘要：
MOTIVATION: Microarray experiments generate large datasets with expression values for thousands of genes but not more than a few dozens of samples. Accurate supervised classification of tissue samples in such high-dimensional problems is difficult but often crucial for successful diagnosis and treatment. A promising way to meet this challenge is by using boosting in conjunction with decision trees. RESULTS: We demonstrate that the generic boosting algorithm needs some modification to become an accurate classifier in the context of gene expression data. In particular, we present a feature preselection method, a more robust boosting procedure and a new approach for multi-categorical problems. This allows for slight to drastic increase in performance and yields competitive results on several publicly available datasets. AVAILABILITY: Software for the modified boosting algorithms as well as for decision trees is available for free in R at http://stat.ethz.ch/~dettling/boosting.html.
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关键词：
Algorithms Artificial Intelligence Gene Expression Profiling Genetic Screening 算法 人工智能 遗传筛选 肿瘤蛋白质类 肿瘤 寡核苷酸序列分析
DOI：
10.1093/bioinformatics/bth447
被引量：
年份：
2004
Taylor & Francis
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