微阵列预测中最优基因集的寻找算法
Algorithm for Finding Optimal Gene Sets in Microarray Prediction
摘要：
动机：微阵列数据最近已被证明在区分经常出现在癌症诊断中的密切相关的细胞类型方面是有效的。确定进行这种诊断所需的最小基因数量对于临床使用和确定特定基因对癌症的重要性都是有用的。这里，复制算法用于此目的。它进化出一组预测因子，所有这些因子都使用不同的基因组合来生成一组最佳预测因子。结果如下：我们应用这种方法的白血病数据的怀特黑德/麻省理工学院的小组，试图区分诊断两种白血病，也Khan等人的数据。来区分四种不同的儿童癌症。在后一种情况下，我们能够将所需的基因数量从96个减少到15个，同时能够完美地对所有测试数据进行分类。可用性：http://stravinsky.ucsc.edu/josh/gesses/联系人：josh@physics.ucsc.edu
英文摘要：
Motivation: Microarray data has been recently been shown to be efficacious in distinguishing closely related cell types that often appear in the diagnosis of cancer. It is useful to determine the minimum number of genes needed to do such a diagnosis both for clinical use and to determine the importance of specific genes for cancer. Here a replication algorithm is used for this purpose. It evolves an ensemble of predictors, all using different combinations of genes to generate a set of optimal predictors. Results: We apply this method to the leukemia data of the Whitehead/MIT group that attempts to differentially diagnose two kinds of leukemia, and also to data of Khan et. al. to distinguish four different kinds of childhood cancers. In the latter case we were able to reduce the number of genes needed from 96 down to 15, while at the same time being able to perfectly classify all of their test data. Availability: http://stravinsky.ucsc.edu/josh/gesses/ Contact: josh@physics.ucsc.edu
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
