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Meta analysis of gene expression data of multiple cancer types to predict biomarkers and drug targets | K.S 1 | Computational Molecular Biology

Meta analysis of gene expression data of multiple cancer types to predict biomarkers and drug targets  

Shashank K.S1 , Mamatha H.R1 , Prashantha C.N2
1 Department of Information Science, PES Institute of Technology, Bangalore, India
2 Department of Biological sciences, Scientific Bio-Minds, Bangalore, India
Author    Correspondence author
Computational Molecular Biology, 2015, Vol. 5, No. 5   doi: 10.5376/cmb.2015.05.0005
Received: 17 Aug., 2015    Accepted: 25 Sep., 2015    Published: 19 Oct., 2015
© 2015 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Shashank K.S., Mamatha H.R., and Prashantha C.N., 2015, Meta Analysis of Gene Expression Data of Multiple Cancer Types To Predict Biomarkers and Drug Targets Interactions in Ovarian Cancer, Computational Molecular Biology, 5(5): 1-9

Abstract

Meta analysis of gene expression data of multiple cancer types such as breast, colon and ovary used to identify gene signatures that functionally used as a marker to prognosis and molecular diagnostics. There is a reliable identification of gene signatures is associated with different cancer types remains a challenge. The aim of this study is to develop microarray statistical data analysis methods and SVM classifiers to identify differentially expressed genes in different cancer types. Using our method to perform 16 datasets such as 6 breast cancer, 4 colon cancer and 6 ovarian cancer of different datasets. Our results is analysed in 4 different methods (a) preprocess the data to identify quality expression of datasets by removing null values and non significant values (p<0.05) (b). Differential gene expression analysis using statistical analysis to predict upregulation and downregulated gene signatures (c) subgrouping of datasets that has been classified based on cancer types (d) gene network prediction to identify gene-gene interaction to understand biological markers. We have predicted 8 markers in breast cancer, 10 markers in colon cancer and 16 markers in ovarian cancer is providing new direction for diagnostics and therapeutic development.

Keywords
breast cancer; Colon cancer; Ovarian cancer; Microarray; Statistics; Limma; Biocoductor; geNETClassifier
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