Algorithm for Microarray Cancer Data Analysis using Frequent Pattern Mining and Gene Intervals

Print
IJCA Proceedings on National Conference on Research Issues in Image Analysis and Mining Intelligence
© 2015 by IJCA Journal
NCRIIAMI 2015 - Number 1
Year of Publication: 2015
Authors:
Alagukumar. S
Lawrance. R

Alagukumar. S and Lawrance. R. Article: Algorithm for Microarray Cancer Data Analysis using Frequent Pattern Mining and Gene Intervals. IJCA Proceedings on National Conference on Research Issues in Image Analysis and Mining Intelligence NCRIIAMI 2015(1):9-14, June 2015. Full text available. BibTeX

@article{key:article,
	author = {Alagukumar. S and Lawrance. R},
	title = {Article: Algorithm for Microarray Cancer Data Analysis using Frequent Pattern Mining and Gene Intervals},
	journal = {IJCA Proceedings on National Conference on Research Issues in Image Analysis and Mining Intelligence},
	year = {2015},
	volume = {NCRIIAMI 2015},
	number = {1},
	pages = {9-14},
	month = {June},
	note = {Full text available}
}

Abstract

Microarray technology allows for the simultaneously monitor of expression levels for thousands of genes or entire genomes. Diseases are often controlled by groups of genes, rather than individual ones. Association rule mining technique in data mining plays a vital role in the field of bioinformatics. In this paper, it has been proposed a novel approach for analysis of microarray gene expression profiling data. It discovers frequent patterns, expressions profiles using transcript expression intervals and extract significant relations among microarray genes. It is important to get efficient and important patterns to reveal fatal and crucial reasons for diseases. It provides improving prediction for diseases and treatment decisions for cancer patients.

References

  • Agarwal. R and Srikant. R (1994)," Fast algorithm for mining association rules in large data bases", Proceedings of the 20th international conference on very Large Data Base (VLDB'94), Santiago, chile, pp 487-499.
  • Han,J. , and Kamber,M. , "Data Mining: Concepts and Techniques", Morgan Kaufmann Publishers, Elsevier, 2002.
  • Garcia S, Luengo J, Sez J, Lpez V, and Herrera F , "A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning". IEEE Transactions on Knowledge and Data Engineering vol. 25, no. 4, pp. 734–750. 2013.
  • Liu, H. , Hussain, F. , Tan, C. L. , and Dash, M. . Discretization: An enabling technique. Data mining and knowledge discovery, vol. 6, no. 4, pp. 393-423, 2002.
  • Becquet C, Blachon S, and Jeudy B, et al. "Strong-association-rule mining for large-scale gene-expression data analysis:a case study on human SAGE data". Genome Biology, pp. 3:12. 2002.
  • McIntosh T, and Chawla S. ,"High confidence rule miningfor microarray analysis". IEEE/ACM Transactions, Computational Biology and Bioinformatics; vol. 4, no. 4, pp. 611–23, 2007.
  • Zakaria, W. , Kotb, Y. , andGhaleb, F. , "MCR-Miner: Maximal Confident Association Rules Miner Algorithm for Up/Down-Expressed Genes". Appl. Math, vol. 8, no. 2, pp. 799-809, 2014.
  • Agrawal,R. , Imielinski,T. , and Swami,A. , "Mining association rules between sets of items in large databases". In: Proceedings of the 1993 ACMSIGMOD International Conference on Management of Data. Washington, DC, USA: ACM Press, pp. 207–216, 1993.
  • Tuimala,J. , and Laine,M. M. , "DNA Microarray Data Analysis", Second Edition, PicasetOy, Helsinki, 2005.
  • http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE1379.
  • Ghilardi G, Biondi M, La Torre A, Battaglioli L, Scorza R (2005) Breast cancer progression and host polymorphisms in the chemokine system: role of the macrophage chemoattractant protein-1 (mcp-1)-2518 g allele. Clinical Chemistry 51: 452–5.