Title

Colon cancer prediction with Genetic profiles using Intelligent techniques

Authors

Subha Mahadevi Alladi ¹,Shinde Santosh P.¹, Vadlamani. Ravi^2,* and Upadhyayula Suryanarayana Murthy¹

Affiliation

¹Bioinformatics Group, Biology Division, Indian Institute of Chemical Technology, Tarnaka, Hyderabad 500007, Andhra Pradesh, India; ²Institute for Development and Research in Banking Technology, Castle Hills Road, Masab Tank, Hyderabad 500057, India

Article Type

Current Trends

Date

received June 10, 2008; revised August 28, 2008; accepted September 13, 2008; published November 04, 2008

Abstract

Micro array data provides information of expression levels of thousands of genes in a cell in a single experiment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In our present study we have used the benchmark colon cancer data set for analysis. Feature selection is done using t –statistic. Comparative study of class prediction accuracy of 3 different classifiers viz., support vector machine (SVM), neural nets and logistic regression was performed using the top 10 genes ranked by the t –statistic. SVM turned out to be the best classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy. Logistic Regression ranks as the next best classifier followed by Multi Layer Perceptron (MLP). The top 10 genes selected by us for classification are all well documented for their variable expression in colon cancer. We conclude that SVM together with t-statistic based feature selection is an efficient and viable alternative to popular techniques.

Keywords

gene expression; tumor classification; t-statistic; feature selection; SVM neural network; logistic regression

Citation

Alladi et al., Bioinformation 3(3): 130-133 (2008)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.