Disease-Related Gene Expression Analysis Using an Ensemble Statistical Test Method
The development of novel high-throughput experimental techniques makes it possible to comprehensively analyze biological data in health and disease. However, a large amount of data generated results in dramatic data-analytic challenges in discovery of ‘signature’ molecules, which are specific to different biological conditions (e.g. normal vs. disease, treated vs. untreated). Current statistical methods are effective only in the case their hypothesis can be matched. In this paper, we apply an ensemble statistical method to infer significant molecules. In our approach, four well-done and well-understanding statistical techniques had been used for the analysis to the experimental data, and then the results will be collected into an ensemble framework to find the high confident “significant” molecules which can distinguish the different experimental conditions. We evaluate the performance of our approach on a test dataset which deposited on GEO database with an access number of GSE45114.
KeywordsStatistical Tests Ensemble framework signature molecules gene expression profile
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- 1.Baumgartner, C., Osl, M., Netzer, M., Baumgartner, D.: Bioinformatic-driven search for metabolic biomarkers in disease. J. Clin Bioinformatics 1(2) (2011), doi:10.1186/2043-9113-1181-1182Google Scholar
- 9.Deng, X., Geng, H., Ali, H.H.: Cross-platform analysis of cancer biomarkers: a Bayesian network approach to incorporating mass spectrometry and microarray data. Cancer Informatics 3, 183–202 (2007)Google Scholar
- 12.Chi, C.L., Street, W.N., Wolberg, W.H.: Application of artificial neural network-based survival analysis on two breast cancer datasets. In: AMIA.. Annual Symposium Proceedings/AMIA Symposium, pp. 130–134 (2007)Google Scholar