Minimal gene selection for classification and diagnosis prediction based on gene expression profile


1 Medical School, Medical Physics and Engineering, Medical Image and Signal Processing Research Center; Isfahan University of Medical Sciences, Isfahan, Iran

2 Medical School, Medical Physics and Engineering, Medical Image and Signal Processing Research Center, Isfahan, Iran


Background: Up to date different methods have been used in order to dimensions reduction, classification, clustering and prediction of cancers based on gene expression profiling. The aim of this study is extracting most significant genes and classifying of Diffuse Large B-cell Lymphoma (DLBCL) patients on the basis of their gene expression profiles.
Materials and Methods: We studied 40 DLBCL patients and 4026 genes. We utilized Artificial Neural Network (ANN) for classification of patients in two groups: Germinal center and Activated like. As we were faced with low number of patients (40) and numerous genes (4026), we tried to deploy one optimum network and achieve to minimum error. Moreover we used signal to noise (S/N) ratio as a main tool for dimension reduction. We tried to select suitable training data and so to train just one network instead of 26 networks. Finally, we extracted two most significant genes.
Result: In this study two most significant genes based on their S/N ratios were selected. After selection of suitable training samples, the training and testing error were 0 and 7% respectively.
Conclusion: We have shown that the use of two most significant genes based on their S/N ratios and selection of suitable training samples can lead to classify DLBCL patients with a rather good result. Actually with the aid of mentioned methods we could compensate lack of enough number of patients, improve accuracy of classifying and reduce complication of computations and so running time.


1. Khan J, Wei JS, Ringner M, Sall LH, Landanyi M, Wetermann F, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001;7:673-9.  Back to cited text no. 1
2. Nguyen DV, Arpat AB, Wang N, Carroll RJ. DNA Microarray experiments: Biological and technological aspects. Biometrics 2002;58:701-17.  Back to cited text no. 2
3. Lossos IS, Morgensztern D. Non-Hodgkin's lymphoma in the microarray era. Clin Lymphoma 2004;5:128-9.  Back to cited text no. 3
4. Liu J, Iba H, Ishizuka M. Selecting informative genes with parallel genetic algorithms in tissue classification. Genome Inform 2001;12:14-23.  Back to cited text no. 4
5. Mehridehnavi AR. Classification of different cancerous animal tissues on the basis of their 1H NMR spectra data using different types of artificial neural networks. Res Pharm Sci 2007;2:53-9.  Back to cited text no. 5
6. Alizadeh A, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000;403:503-11.  Back to cited text no. 6
7. O' Neill MC, Song LI. Neural Network analysis of lymphoma microarray data: Prognosis and diagnosis near perfect. BMC Bioinform 2003;4:13.  Back to cited text no. 7
8. Lossos IS, Czerwinski DK, Alizadeh A, Wechser MA, Tibshirani R, Botstein D, et al. Prediction of survival in Diffuse Large B-Cell lymphma based on the expression of six genes. N Engl J Med 2004;350:1828-37.  Back to cited text no. 8
9. Ziaei L, Mehri AR, Salehi M. Application of artificial neural networks in classification and diagnosis prediction of a subtype of lymphoma based on gene expression profile. JRMS 2006;11:13-7.  Back to cited text no. 9
10. Gloub TR, Slonim DK, Jamayo P, Gaasenbeek M, Huard C, Mesirov JP, et al. Molecular Classification of cancer: Class discovery and class prediction by gene expression monitoring Science 1999;286:531-7.  Back to cited text no. 10