Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Finding Informative Genes From Microarray Gene Expression Data Using FIGER-test

  • Published : 2007.10.25
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Abstract

Microarray gene expression data is believed to show the functions of living organism through the gene expression values. We have studied a method to get the informative genes from the microarray gene expression data. There are several ways for this. In recent researches to get more sophisticated and detailed results, it has used the intelligence information theory like fuzzy theory. Some methods are to add fudge factors to the significance test for more refined results. In this paper, we suggest a method to get informative genes from microarray gene expression data. We combined the difference of means between two groups and the fuzzy membership degree which reflects the variance of the gene expression data. We have called our significance test the Fuzzy Information method for Gene Expression data(FIGER). The FIGER calculates FIGER variation ratio and FIGER membership degree to show how strongly each object belongs to the each group and then it results in the significance degree of each gene. The FIGER is focused on the variation and distribution of the data set to adjust the significance level. Out simulation shows that the FIGER-test is an effective and useful significance test.

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References

  1. http://www.gene-chips.com/
  2. L.R.Liang, S.Lu, X.Wang, Y.Lu, V. Mandai, D.Patacsil, D. Kumar, 'FM-test: a fuzzy- set-theory-based approach to differential gene expression data analysis,' BMC Bioinformatics, Vol. 7, Suppl No.4, S7, 2006
  3. V.G.Tusher, R.Tibshirani, G.Chu, 'Significance analysis of microarrays applied to the ionizing radiation response' PNAS, Vol.98, No.9, pp 5116 - 5121, 2001 https://doi.org/10.1073/pnas.091062498
  4. http://www.ncbi.nlm.nih.gov/
  5. ftp://ftp.ncbi.nih.gov/pub/geo/
  6. http://www.r-proiect.org/
  7. R. Arya, J. Blangero, K. Williams, L. Almasy, T.D. Dyer, R.J.Leach, P.O'Connell, M.P.Stern, R.Duggirala, 'Factors of Insulin Resistance Syndrome-Related Phenotypes Are Linked to Genetic Locations on Chromosomes 6 and 7 in Nondiabetic Mexican-Americans' DIABETES, Vol. 51, No.3, pp 841-847, 2002 https://doi.org/10.2337/diabetes.51.3.841
  8. Sholom M. Weiss, Nitin Indurkhya, Predictive Data Mining A Practical Guide, Morgan Kaufmann Publishers, 1998
  9. Leonard kaufman, Peter J. Rousseeuw, Finding Groups in Data, Wiley series in probability and statistics, 2005
  10. Yuanchen He, Yuchun Tang, Yan-Qing Zhang, Raishekar Sunderraman, 'Fuzzy- Granular Gene Selection from Microarray Expression Data', Sixth IEEE International Conference on Data Mining Workshops (ICDMW'06), 2006