Classification of intramural metastases and lymph node metastases of esophageal cancer from gene expression based on boosting and projective adaptive resonance theory
Classification of intramural metastases and lymph node metastases of esophageal cancer from gene expression based on boosting and projective adaptive resonance theory
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DOI：
10.1263/jbb.102.46
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发表时间：
2006-07-01
影响因子：
2.8
通讯作者：
Honda, Hiroyuki
中科院分区：
文献类型：
--
作者：
Takahashi, Hiro;Aoyagi, Kazuhiko;Honda, Hiroyuki
食道癌是一种众所周知的癌症，与其他癌症相比预后较差。为了提高癌症患者的治疗水平，迫切需要一种基于准确诊断的最佳个体化治疗方案。为此，重要的是开发一种复杂的算法来管理大量数据，例如来自DNA微阵列的基因表达数据，以实现最佳和个性化的诊断。在基因表达数据分析中，标记基因的选择是必不可少的。我们已经发展了一种结合使用射影自适应共振理论和Boost模糊分类器的方法，其中扫描算子表示为PART-BFCS。该方法优于其他方法，具有计算快速、预测准确、预测可靠和规则提取四个特点。在这项研究中，我们应用这种方法来分析从食道癌患者获得的微阵列数据。探讨了部分BFCS与U检验相结合的方法。因为食道癌数据非常复杂，所以有必要使用一种特定类型的BFCS，即BFCS-1，2。基于U-检验模型的部分BFCS和部分BFCS比传统的k-近邻(KNN)和加权投票(WV)方法具有更好的分类性能。我们的方法可以发现包括CDK6在内的基因，并可以提取出优秀的IF-THEN规则。本研究筛选出的基因具有作为食道癌新诊断标记物的潜力。这些结果表明，新的方法可以用于癌症患者诊断的标记基因选择。
Esophageal cancer is a well-known cancer with poorer prognosis than other cancers. An optimal and individualized treatment protocol based on accurate diagnosis is urgently needed to improve the treatment of cancer patients. For this purpose, it is important to develop a sophisticated algorithm that can manage a large amount of data, such as gene expression data from DNA microarrays, for optimal and individualized diagnosis. Marker gene selection is essential in the analysis of gene expression data. We have already developed a combination method of the use of the projective adaptive resonance theory and that of a boosted fuzzy classifier with the SWEEP operator denoted PART-BFCS. This method is superior to other methods, and has four features, namely fast calculation, accurate prediction, reliable prediction, and rule extraction. In this study, we applied this method to analyze microarray data obtained from esophageal cancer patients. A combination method of PART-BFCS and the U-test was also investigated. It was necessary to use a specific type of BFCS, namely, BFCS-1,2, because the esophageal cancer data were very complexity. PART-BFCS and PART-BFCS with the U-test models showed higher performances than two conventional methods, namely, k-nearest neighbor (kNN) and weighted voting (WV). The genes including CDK6 could be found by our methods and excellent IF-THEN rules could be extracted. The genes selected in this study have a high potential as new diagnosis markers for esophageal cancer. These results indicate that the new methods can be used in marker gene selection for the diagnosis of cancer patients.