Un-Normalized Graph P-Laplacian Semi- Supervised Learning Method Applied to Cancer Classification Problem

A successful classification of different tumor types is essential for successful treatment of cancer. However, most prior cancer classification methods are clinical-based and have inadequate diagnostic ability. Cancer classification using gene expression data is very important in cancer diagnosis and drug discovery. The introduction of DNA microarray techniques has made simultaneous monitoring of thousands of gene expression probable. With this abundance of gene expression data nowadays, the researchers have the opportunity to do cancer classification using gene expression data. In recent years, a lot of machine learning methods have been proposed to do cancer classification using gene expression data such as clustering-based methods, k-nearest neighbor method, artificial neural network method, and support vector machine method, to name a few. In this paper, we present the un-normalized graph p-Laplacian semisupervised learning methods. These methods will be applied to the patient-patient network constructed from the gene expression data to predict the tumor types of all patients in the network. These methods are based on the assumption that the labels of two adjacent patients in the network are likely to be the same. The experiments show that that the un-normalized graph p-Laplacian semi-supervised learning methods are at least as good as the current state of the art network-based method (the un-normalized graph Laplacian based semi-supervised learning method) but often lead to better classification accuracy performance measures.