Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 158 countries worldwide.
Application of Microarray Technology and softcomputing in cancer Biology
K.Vaishali, A.VinayaBabu
Pages - 225 - 233     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
DNA Microarray, Classification, Soft Computing, Gene Expression, Data Mining
DNA microarray technology has emerged as a boon to the scientific community in understanding the growth and development of life as well as in widening their knowledge in exploring the genetic causes of anomalies occurring in the working of the human body. microarray technology makes biologists be capable of monitoring expression of thousands of genes in a single experiment on a small chip. Extracting useful knowledge and info from these microarray has attracted the attention of many biologists and computer scientists. Knowledge engineering has revolutionalized the way in which the medical data is being looked at. Soft computing is a branch of computer science capable of analyzing complex medical data. Advances in the area of microarray –based expression analysis have led to the promise of cancer diagnosis using new molecular based approaches. Many studies and methodologies have come up which analyszes the gene espression data by using the techniques in data mining such as feature selection, classification, clustering etc. emboiding the soft computing methods for more accuracy. This review is an attempt to look at the recent advances in cancer research with DNA microarray technology , data mining and soft computing techniques.
CITED BY (2)  
1 Biswas, P., Barman, B., & Mukhopadhyay, A. (2015). Construction of Co-expression and Co-regulation Network with Differentially Expressed Genes in Bone Marrow Stem Cell Microarray Data. In Information Systems Design and Intelligent Applications (pp. 761-769). Springer India.
2 Raza, K., & Jaiswal, R. (2013). Reconstruction and Analysis of Cancer-specific Gene Regulatory Networks from Gene Expression Profiles. arXiv preprint arXiv:1305.5750.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 refSeek 
5 iSEEK 
6 Bielefeld Academic Search Engine (BASE) 
7 Scribd 
8 SlideShare 
9 PdfSR 
1 Nguyen and Rocke, Classification of Acute Leukemia based on DNA Micro array Gene Expressions using Partial Least Squares, Kluwer Academic, Dordrecht, 2001
2 Jian J. Dai, Linh Lieu, and David Rocke, "Dimension Reduction for Classification with Gene Expression Micro array Data", Statistical Applications in Genetics and Molecular Biology: Vol. 5, No. 1, 2006
3 Alok Sharma and Kuldip K. Paliwal, "Cancer classification by gradient LDA technique using micro array gene expression data", Data & Knowledge Engineering, Vol. 66, pp. 338-347, 2008
4 Chun-Hou Zheng, Bo Li, Lei Zhang and Hong-Qiang Wang, "Locally Linear Discriminant Embedding for Tumor Classification", In Proceedings of ICIC, pp.1093-1100, 2008
5 Cheng-San Yang, Li-Yeh Chuang, Chao-Hsuan Ke and Cheng-Hong Yang, "A hybrid Feature Selection Method for Micro array Classification", International Journal of Computer Science, Vol. 35, No. 3, 2008
6 Danh V. Nguyen, David M. Rocke, "Tumor Classification by Partial Least Squares Using Micro array Gene Expression Data", Bioinformatics, Vol. 18, No. 1, pp. 39-50, 2002
7 Pengyi Yang and Zili Zhang, "An Embedded Two-Layer Feature Selection Approach for Microarray Data Analysis", IEEE Intelligent Informatics Bulletin, Vol.10, No.1, pp. 24-32, 2009
8 Yuh-Jye Lee and Chia-Huang Chao, "A Data Mining Application to Leukemia Micro array Gene Expression Data Analysis", International Conference on Informatics, Cybernetics and Systems (ICICS), Kaohsiung, Taiwan, 2003
9 James J. Chen and Chun-Houh Chen, "Micro array Gene Expression", Encyclopedia of Biopharmaceutical Statistics, 2nd Edition, Marcel Dekker, Inc., pp. 599-613, 2003
10 Seeja and Shweta, "Microarray Data Classification Using Support Vector Machine", International Journal of Biometrics and Bioinformatics (IJBB), Vol. 5, No. 1, pp. 10-15, 2011
11 Yee Hwa Yang and Natalie P. Thorne, "Normalization for Two-color cDNA Microarray Data", Science and Statistics: A Festschrift for Terry Speed, Vol. 40, pp. 403-418, 2003
12 Fei Pana, Baoying Wanga, Xin Hub and William Perrizoa, "Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis", Journal of Biomedical Informatics, Vol. 37, pp. 240–248, 2004. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gassenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring”, Science, 286(15):531–537, 1999.
13 Terrence S. Furey, Nello Cristianini, Nigel Duffy, David W. Bednarski, Michèl Schummer, and David Haussler, “Support vector machine classification and validation of cancer tissue samples using microarray expression data “, Bioinformatics6(10): 906-914 , 2000
14 Zhang, X. and Ke, H.,” ALL/AML cancer classification by gene expression data using SVM and CSVM approach”, Genome Informatics, Universal Academy Press, pp. 237-239, 2000.
15 Xin Zhao, Leo Wang-Kit Cheung, “Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data”, BMC Bioinformatics.,8:67,2007.
16 Wenlong Xu, Minghui Wang, Xianghua Zhang, Lirong Wang, Huanqing Feng,” SDED: Anovel filter method for cancer-related gene selection”, Bioinformation 2(7): 301-303,2008.
17 D.P. Berrar, C.S. Downes, W. Dubitzky, “Multiclass Cancer Classification Using Gene Expression Profiling and Probabilistic Neural Networks”, Pacific Symposium on Biocomputing 8:5-16, 2003.
18 Golub TR, Slonim DK, Tamayo P, et al. Molecular classifi cation of cancer: class discovery and class prediction by gene expression monitoring. Science 1999 ; 286 : 531 -7
19 Perou CM, Sørlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000 406 : 747 -52
20 Alon U, Barkai N, Notterman DA, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 1999 ; 96 : 6745 -50
21 Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 2002 ; 99 : 6567 -72
22 Ahmad M. Sarhan, "Cancer Classification Based on Micro array Gene Expression Data Using DCT and ANN", Journal of Theoretical and Applied Information Technology, Vol. 6, No. 2, pp. 208-216, 2009
23 Bharathi and Natarajan, "Cancer Classification of Bioinformatics data using ANOVA", International Journal of Computer Theory and Engineering, Vol. 2, No. 3, pp. 369-373, June 2010
24 Bo Li, Chun-Hou Zheng, De-Shuang Huang, Lei Zhang and Kyungsook Han, “"Gene expression data classification using locally linear discriminant embedding", Computers in Biology and Medicine, Vol. 40, pp. 802–810, 2010
25 Xiaosheng Wang and Osamu Gotoh, "A Robust Gene Selection Method for Micro array-based Cancer Classification", Journal of Cancer Informatics, Vol. 9, pp. 15-30, 2010
26 Mallika and Saravanan, "An SVM based Classification Method for Cancer Data using Minimum Micro array Gene Expressions", World Academy of Science, Engineering and Technology, Vol. 62, No. 99, pp. 543-547, 2010
27 Chhanda Ray, "Cancer Identification and Gene Classification using DNA Micro array Gene Expression Patterns", International Journal of Computer Science Issues, Vol. 8, Issue 2, pp. 155-160, March 2011.
28 S. Mitra, “An evolutionary rough partitive clustering,” Pattern Recognit. Lett., vol. 25, pp. 1439–1449, 2004
29 S. B. Cho and J. Ryu, “Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features,” Proc. IEEE, vol. 90, no. 11, pp. 1744–1753, Nov. 2002.
30 S. Bicciato,M. Pandin, G. Didon`e, andC.DiBello, “Pattern identification and classification in gene expression data using an autoassociative neural network model,” Biotechnol. Bioeng., vol. 81, pp. 594–606, 2003.
31 F. Chu, W. Xie, and L. Wang, “Gene selection and cancer classification using a fuzzy neural network,” in Proc. 2004 Annu. Meet. North Amer. Fuzzy Information Processing Soc. (NAFIPS), vol. 2, pp. 555–559.
32 K. Deb and A. Raji Reddy, “Reliable classification of two-class cancer data using evolutionary algorithms,” BioSystems, vol. 72, pp. 111–129, 2003.
33 H. Midelfart, J. Komorowski, K. Nørsett, F. Yadetie, A. K. Sandvik,and A. Lægreid, “Learning rough set classifiers from gene expression and clinical data,” Fundamenta Inf., vol. 53, pp. 155–183, 2002.
34 M. E. Futschik, A. Reeve, and N. Kasabov, “Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue,” Artif. Intell. Med., vol. 28, pp. 165–189, 2003.
35 M. Banerjee, S. Mitra, and H. Banka, “Evolutionary-rough feature selection in gene expression data,” IEEE Trans. Syst., Man, Cybern. C, Appl.
37 Fei Pana, Baoying Wanga, Xin Hub and William Perrizoa, "Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis", Journal of Biomedical Informatics, Vol. 37, pp. 240–248, 2004
38 H. Midelfart, A. Lægreid, and J. Komorowski, Classification of Gene Expression Data in an Ontology, vol. 2199. Lecture Notes in Computer Science, Berlin, Germany: Springer-Verlag, 2001, pp. 186–194
Associate Professor K.Vaishali
JITS - India
Dr. A.VinayaBabu
Jntu,HYD - India