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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 826373, 10 pages
http://dx.doi.org/10.1155/2014/826373
Research Article

NIM: A Node Influence Based Method for Cancer Classification

College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Received 10 March 2014; Revised 16 June 2014; Accepted 23 June 2014; Published 11 August 2014

Academic Editor: Shengyong Chen

Copyright © 2014 Yiwen Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The classification of different cancer types owns great significance in the medical field. However, the great majority of existing cancer classification methods are clinical-based and have relatively weak diagnostic ability. With the rapid development of gene expression technology, it is able to classify different kinds of cancers using DNA microarray. Our main idea is to confront the problem of cancer classification using gene expression data from a graph-based view. Based on a new node influence model we proposed, this paper presents a novel high accuracy method for cancer classification, which is composed of four parts: the first is to calculate the similarity matrix of all samples, the second is to compute the node influence of training samples, the third is to obtain the similarity between every test sample and each class using weighted sum of node influence and similarity matrix, and the last is to classify each test sample based on its similarity between every class. The data sets used in our experiments are breast cancer, central nervous system, colon tumor, prostate cancer, acute lymphoblastic leukemia, and lung cancer. experimental results showed that our node influence based method (NIM) is more efficient and robust than the support vector machine, K-nearest neighbor, C4.5, naive Bayes, and CART.