癌症基因分类的Laplace谱方法

摘要

We introduce a novel classification algorithm for gene expression data based on the Laplacian spectra of graphs. The class center is obtained by computing the average of each class in the trailing set, and the Laplacian matrices of complete graphs so called normal graphs are constructed on some samples with the minimum Euclidean distance between the class center.The sum of matched points are calculated by replacing points of standard image with test samples. The test sample is divided into the biggest one of the total matched points of the class. The effectiveness of this algorithm has been verified through the leaving-one experiments using Leukemia data and Colon cancer data.%本文尝试着将图的Laplace谱理论应用于癌症基因表达谱数据的分类上.计算出训练集中每个类的均值作为类中心,选出与类中心欧式距离最小的若干样本用laplace矩阵构造完全图,记为代表该类的标准图.用待测样本依次替换标准图中所有的点,将生成的新图与标准图进行特征点匹配,并计算匹配点数总和.将待测样本划分为总匹配点数最多的那个类.通过对白血病两个亚型(ALL与AML)与结肠癌数据进行留一法实验,验证了本文方法的有效性.