Cancer Identification and Gene Classification using DNA Microarray Gene Expression Patterns

DNA microarray gene expression patterns of several model organisms provide a fascinating opportunity to explore important abnormal biological phenomena. The development of cancer is a multi-step process in which several genes and other environmental and hormonal factors play an important role. In this paper, a new algorithm is proposed to analysis DNA microarray gene expression patterns efficiently for huge amount of DNA microarray data. For better visibility and understanding, experimental results of DNA microarray gene pattern analysis are represented graphically. The shape of each graph corresponding to a DNA microarray gene expression pattern is determined by using an eight-directional chain code sequence, which is invariant to translation, scaling, and rotation. The cancer development is identified based on the variations of DNA microarray gene expression patterns of the same organism by simultaneously monitoring the expression of thousand of genes. At the end, classification of cancer genes is also focused based on the distribution probability of codes of the eight-directional chain code sequences representing DNA microarray gene expression patterns and the experimental result is provided.

[1]  Obi L. Griffith,et al.  cis-Regulatory element prediction in mammalian genomes , 2005, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05).

[2]  A. Tewfik,et al.  Signal Processing Techniques and Statistics for the Analysis of Human Genome Associated with Behavior Abnormalities , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[3]  C. Croce,et al.  Genome Wide Identification of Recessive Cancer Genes by Combinatorial Mutation Analysis , 2008, PloS one.

[4]  Peter J. Russell,et al.  Fundamentals of Genetics , 1994 .

[5]  Ranjan Jana,et al.  Image Registration Using Object Shape's Chain Code , 2009, 2009 2nd International Congress on Image and Signal Processing.

[6]  Armando Blanco,et al.  Unveiling Fuzzy Associations Between Breast Cancer Prognostic Factors and Gene Expression Data , 2009, 2009 20th International Workshop on Database and Expert Systems Application.

[7]  Hidemitsu Nakamura,et al.  Self-Organizing Clustering: A Novel Non-Hierarchical Method for Clustering Large Amount of DNA Sequences , 2003 .

[8]  Jianying Li,et al.  Microarray gene expression profile data mining model for clinical cancer research , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[9]  F. Azuaje,et al.  Gene expression patterns and cancer classification: a self-adaptive and incremental neural approach , 2000, Proceedings 2000 IEEE EMBS International Conference on Information Technology Applications in Biomedicine. ITAB-ITIS 2000. Joint Meeting Third IEEE EMBS International Conference on Information Technol.

[10]  Meanshift clustering for DNA microarray analysis , 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

[11]  Shamkant B. Navathe,et al.  Investigation into biomedical literature classification using support vector machines , 2005, 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05).

[12]  Jing Li,et al.  Identifying Gene Signatures from Cancer Progression Data Using Ordinal Analysis , 2009, 2009 IEEE International Conference on Bioinformatics and Biomedicine.

[13]  Jian Su,et al.  CpG-discover: A machine learning approach for CpG islands identification from human DNA sequence , 2009, 2009 International Joint Conference on Neural Networks.