This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Accurate Cancer Classification Using Expressions of Very Few Genes
January-March 2007 (vol. 4 no. 1)
pp. 40-53
We aim at finding the smallest set of genes that can ensure highly accurate classification of cancers from microarray data by using supervised machine learning algorithms. The significance of finding the minimum gene subsets is three-fold: 1) It greatly reduces the computational burden and "noise” arising from irrelevant genes. In the examples studied in this paper, finding the minimum gene subsets even allows for extraction of simple diagnostic rules which lead to accurate diagnosis without the need for any classifiers. 2) It simplifies gene expression tests to include only a very small number of genes rather than thousands of genes, which can bring down the cost for cancer testing significantly. 3) It calls for further investigation into the possible biological relationship between these small numbers of genes and cancer development and treatment. Our simple yet very effective method involves two steps. In the first step, we choose some important genes using a feature importance ranking scheme. In the second step, we test the classification capability of all simple combinations of those important genes by using a good classifier. For three "small” and "simple” data sets with two, three, and four cancer (sub)types, our approach obtained very high accuracy with only two or three genes. For a "large” and "complex” data set with 14 cancer types, we divided the whole problem into a group of binary classification problems and applied the 2--step approach to each of these binary classification problems. Through this "divide-and-conquer” approach, we obtained accuracy comparable to previously reported results but with only 28 genes rather than 16,063 genes. In general, our method can significantly reduce the number of genes required for highly reliable diagnosis.
Index Terms:
Cancer classification, gene expression, fuzzy, neural networks, support vector machines.
Citation:
Lipo Wang, Feng Chu, Wei Xie, "Accurate Cancer Classification Using Expressions of Very Few Genes," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 1, pp. 40-53, Jan.-March 2007, doi:10.1109/TCBB.2007.1006
Usage of this product signifies your acceptance of the Terms of Use.