The Classification of Gene Expression Profile Based on the Adjacency Matrix Spectral Decomposition
In the process of disease diagnosis, determining the types of disease is very important. With the development of DNA microarray technology, the research on huge gene expression profile has become the focus of disease classification. This paper presents a method of classification of gene expression profile based on the adjacency matrix spectral decomposition. First, samples are mapped to a high-dimensional space of points to construct an adjacency matrix, and we can obtain eigenvectors describing the feature of the samples by decomposing the matrix. Finally, use eigenvectors as inputs of the SVM (Support Vector Machine) and KNN (K nearest neighbor) classifiers to classify gene expression profile. In this way sample information can be completely preserved, which enables an approach to making gene expression profile from one without structural information to one with structural information. The validity of this method is verified by comparative experiments.
classification gene expression profile eigenvector adjacency matrix
Su Liangliang Wang Nian Tang Jun Chen Le Wang Ruiping
Education Ministry Key Laboratory of Intelligent Computing & Signal Processing, Anhui University Hefei, Anhui, 230039, Peoples Republic of China
国际会议
太原
英文
542-546
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）