小圆蓝细胞瘤预测模型研究

为研究肿瘤与基因之间的关系,分析了小圆蓝细胞瘤基因表达数据,建立了分类和预测小圆蓝细胞瘤4个亚型的多模预测模型.针对小圆蓝细胞瘤的4个亚型,该预测模型创建了4个基于神经网络的分类器,并编码4个分类器的分类结果,获得每个数据样本的最终预测结果.研究表明,将复杂的多类分类问题分解为多个2类分类问题是解决多类分类问题的有效方法,基于该方法建立的多模预测模型能够学习基因表达数据中蕴含的知识,并利用获得的知识准确地分类和预测全部83个数据样本.

Research on Models for SRBCT Prediction

In order to study the relation between tumor and gene, the authors analyze the small round blue cell tumor(SRBCT) gene expression data and establishes multi-models prediction model( MMPM) for classifying and predicting four families of SRBCT. MMPM creates four classifiers based on Neural Networks to classify four families and codes results of four classifiers to final prediction consequence for each sample. Research indicates that to divide multi-class classification into several binary classifications is an effective method solving multi-class classification and that MMPM based on this method can learn knowledge from gene expression data and can exactly classify and predict all samples.