Applying CBR Systems to Micro Array Data Classification

Microarray technology allows to measureing the expression levels of thousands of genes in an experiment. This technology required requires computational solutions capable of dealing with great amounts of data and as well as techniques to explore the data and extract knowledge which allow patients classification. This paper presents a systems based on Case-based reasoning (CBR) for automatic classification of leukemia patients from microarray data. The system incorporates novel algorithms for data mining that allow to filter and classify as well as extraction of knowledge. The system has been tested and the results obtained are presented in this paper.

[1]  T. Martínez,et al.  Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps , 1993 .

[2]  Juan M. Corchado,et al.  gene‐CBR: A CASE‐BASED REASONIG TOOL FOR CANCER DIAGNOSIS USING MICROARRAY DATA SETS , 2006, Comput. Intell..

[3]  Vladimir Svetnik,et al.  STATISTICAL ANALYSIS OF HIGH DENSITY OLIGONUCLEOTIDE ARRAYS: A SAFER APPROACH , 2001 .

[4]  John Quackenbush,et al.  Computational genetics: Computational analysis of microarray data , 2001, Nature Reviews Genetics.

[5]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[6]  Juan M. Corchado,et al.  Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems , 2003, ICCBR.

[7]  Jianping Li,et al.  A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue , 2007, Artif. Intell. Medicine.

[8]  John Quackenbush Microarray analysis and tumor classification. , 2006, The New England journal of medicine.

[9]  N. Saitou,et al.  The neighbor-joining method: a new method for reconstructing phylogenetic trees. , 1987, Molecular biology and evolution.

[10]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[11]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[12]  C. Pui,et al.  Lack of benefit of early detection of relapse after completion of therapy for acute lymphoblastic leukemia , 2005, Pediatric blood & cancer.

[13]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[14]  Lawrence M. Fagan,et al.  Medical informatics: computer applications in health care and biomedicine (Health informatics) , 2003 .

[15]  Mark A. van de Wiel,et al.  Microarray Data Analysis: From Hypotheses to Conclusions Using Gene Expression Data , 2004, Cellular oncology : the official journal of the International Society for Cellular Oncology.

[16]  J. V. Moran,et al.  Initial sequencing and analysis of the human genome. , 2001, Nature.

[17]  Roberto Brunelli,et al.  Histograms analysis for image retrieval , 2001, Pattern Recognit..

[18]  I. Jolliffe Principal Component Analysis , 2002 .

[19]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[20]  Peter H. A. Sneath,et al.  Numerical Taxonomy: The Principles and Practice of Numerical Classification , 1973 .

[21]  Donald Michie,et al.  Expert systems in the micro-electronic age , 1979 .

[22]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[23]  C. Ouzounis,et al.  Recent developments and future directions in computational genomics , 2000, FEBS letters.