Identification of disease-causing genes using microarray data mining and Gene Ontology.
BACKGROUND
One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes.


METHODS
We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results.


RESULTS
The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth.


CONCLUSIONS
The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers.
Intelligent Databases, Data mining and Bioinformatics Laboratory, Isfahan University of Technology, Isfahan, Iran. amohammadi@ec.iut.ac.ir
Azadeh
A
Mohammadi
Mohammad H
MH
Saraee
Mansoor
M
Salehi
D003110:Colonic Neoplasms / Q000235:genetics; D057225:Data Mining / Q000379:methods*; D015870:Gene Expression; D005796:Genes; D006801:Humans; D016403:Lymphoma, Large B-Cell, Diffuse / Q000235:genetics; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011471:Prostatic Neoplasms / Q000235:genetics; D012984:Software
Artif Intell Med. 2005 Sep-Oct;35(1-2):185-94
16026974
IEEE Trans Nanobioscience. 2010 Mar;9(1):31-7
19884101
J Bioinform Comput Biol. 2005 Apr;3(2):185-205
15852500
Brief Bioinform. 2010 Jan;11(1):127-41
19789265
Science. 1999 Oct 15;286(5439):531-7
10521349
Proc Natl Acad Sci U S A. 1999 Jun 8;96(12):6745-50
10359783
Artif Intell Med. 2004 Jun;31(2):91-103
15219288
Pac Symp Biocomput. 2001;:52-63
11262969
Pac Symp Biocomput. 2003;:53-64
12603017
Nature. 2000 Feb 3;403(6769):503-11
10676951
Cancer Cell. 2002 Mar;1(2):203-9
12086878
Proc Natl Acad Sci U S A. 2002 May 14;99(10):6562-6
11983868
Comput Biol Chem. 2010 Aug;34(4):244-50
20888301
Bioinformatics. 2000 Oct;16(10):906-14
11120680
Bioinformation. 2008 Apr 11;2(7):301-3
18478083
Bioinformatics. 2005 Apr 15;21(8):1530-7
15585531
Genomics. 2005 Jan;85(1):16-23
15607418
Bioinformatics. 2007 Oct 1;23(19):2507-17
17720704