Call for Paper - September 2020 Edition
IJCA solicits original research papers for the September 2020 Edition. Last date of manuscript submission is August 20, 2020. Read More

Study on the Correlation Coefficient of Gene Expression by using a Hybrid Intelligent System

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 3
Year of Publication: 2012
Zhi Yuan Chen
Dino Isa
Peter Blanchfield

Zhi Yuan Chen, Dino Isa and Peter Blanchfield. Article: Study on the Correlation Coefficient of Gene Expression by using a Hybrid Intelligent System. International Journal of Computer Applications 50(3):6-10, July 2012. Full text available. BibTeX

	author = {Zhi Yuan Chen and Dino Isa and Peter Blanchfield},
	title = {Article: Study on the Correlation Coefficient of Gene Expression by using a Hybrid Intelligent System},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {3},
	pages = {6-10},
	month = {July},
	note = {Full text available}


Normal Lung and carcinoid are high relative classes in our "Detection and Prediction of Lung Cancer using the zNose with the Support Vector Machine Classifier" project. The mRNA expression level of these two classes was analyzed by using oligonucleotide microarrays. The correlation coefficient measurement results referred to the 20 subclasses (mRNA expression) of normal lung and carcinoid, which were collected from a total of 203 specimens (186 snap-frozen lung tumors and 17 normal lungs). The distinct subclasses (mRNA expressions) are 31687_f_at hemoglobin (?), 31525_s_at hemoglobin (?2), and 31481_s_at thymosin (?10). The Correlation Coefficient reflected the results at 0. 8702, 0. 8935 and 0. 9105 respectively (SMOreg PolyKernel -E 1. 0). This study also showed the best prediction class was the first level class which was reflected from the correlation coefficient, recorded at 0. 9409. This result was further verified by the prediction capacity of our proposed system.


  • I. Vergote, J. S. Gordon, A. Elisabeth, G. B. Kristensen, E. Pujade-Lauraine, M. K. B. Parmar and J. B. Vermorken, 2000. New Guidelines to Evaluate the Response to Treatment in Solid Tumors [Ovarian Cancer], Cancer Inst 92: 1534-1535.
  • CM. Kneepkens, C. Ferreira, G. Lepage, et al, 1992. The hydrocarbon breath test in the study of lipid peroxidation: principles and practice, Clin Invest Med 15:163–186.
  • A. Aamodt and E. Plaza, 1994. Case-based reasoning: foundational issues, Methodological variations, and system approaches. AI communications, 7(1), pp. 39-59.
  • W. R. Dillon and M. Goldstein, 1984. Multivariate analysis: methods and applications, Wiley, pp587.
  • N. Cristianini and J. Shawe-Taylor, 2000. An introduction to Support Vector Machines and other kernel-based learning method, Cambridge University press.
  • I. Watson and F. Marir, 1994. Case-Based Reasoning: A Review, The Knowledge Engineering Review, vol. 9, No. 4.
  • H. Ohguro, H. Odagiri, M. Nakazawa et. , 2004. Clinicopathological Features of Gastric Cancer Cases and Aberrantly Expressed Recoverin, The tohoku Journal of experimental medicine,Vol 202, pp213-219.
  • B. Yao, S. N. Rakhade, J. A. Loeb, et al, 2004. Accuracy of cDNA microarray methods to detect small gene expression changes induced by neuregulin on breast epithelial cells, BMC Bioinformatics, Vol 5, pp99.
  • Illumina, 2010. mRNA Expression Analysis, Data Sheet: DNA Analysis, Feb.
  • A. A. Antipova, P. Tamayo and T. R. Golub, 2002. Strategy for oligonucleotide microarray probe reduction, Genome Biology, Volume 3, Issue 12.
  • Z. Y. Chen, D. Isa and P. Blanchfield, 2010. Vectorization Algorithm for an Intelligentized System, International Journal of Computer and Network Security, February issue, Vol. 2 No2.
  • Z. Y. Chen, D. Isa, P. Blanchfield and R. Arelhi, 2010. Improve the Classification and Prediction Performance for the IP Management System in a Super-capacitor Pilot Plant, International Journal of Latest Trends in Computing, Volume 1, Issue 2, December.
  • C. Cortes and V. Vapnik, 1995. Support-vector network. Machine Learning, pp. 273-297.
  • I. H. Witten and E. Frank, 2005. Data Mining: Practical machine learning tools and techniques, 2nd Edition, Kaufmann, Morgan, San Francisco.
  • E. R. House, 1996. Assumptions underlying evaluation models. Assessment & Evaluation in Higher Education, 1469-297X, Vol. 21(4), pp. 347-356.
  • B. Efron and R. Tibshirani, 1997. Improvements on cross-validation: The . 632 + Bootstrap Method. Journal of the American Statistical Association, 92 (438), pp. 548-560.