Document Detail


NIM: A Node Influence Based Method for Cancer Classification.
MedLine Citation:
PMID:  25180045     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
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.
Authors:
Yiwen Wang; Min Yao; Jianhua Yang
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Publication Detail:
Type:  Journal Article     Date:  2014-08-11
Journal Detail:
Title:  Computational and mathematical methods in medicine     Volume:  2014     ISSN:  1748-6718     ISO Abbreviation:  Comput Math Methods Med     Publication Date:  2014  
Date Detail:
Created Date:  2014-09-02     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101277751     Medline TA:  Comput Math Methods Med     Country:  United States    
Other Details:
Languages:  eng     Pagination:  826373     Citation Subset:  IM    
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