The week's pick
Reseach Article
Accurate Cancer Classification using Expressions of Very few Genes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 14 - Number 4 |
| Year of Publication: 2011 |
| Authors: N.Revathy, Dr.R.Amalraj |
10.5120/1832-2452
|
N.Revathy, Dr.R.Amalraj . Accurate Cancer Classification using Expressions of Very few Genes. International Journal of Computer Applications. 14, 4 ( January 2011), 19-22. DOI=10.5120/1832-2452
Abstract
Gene expression profiling by microarray technique has been effectively utilized for classification and diagnostic guessing of cancer nodules. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data. Though, these techniques have not been proposed to deal with the particular needs of gene microarray examination. Initially, microarray data is featured by a high-dimensional feature space repeatedly surpassing the sample space dimensionality by a factor of 100 or higher. Additionally, microarray data contains a high degree of noise. The majority of the existing techniques do not sufficiently deal with the drawbacks like dimensionality and noise. Gene ranking method is later introduced to overcome those problems. Some of the widely used Gene ranking techniques are T-Score, ANOVA, etc. But those techniques will sometimes wrongly predict the rank when large database is used. To overcome these issues, this paper proposes a technique called Enrichment Score for ranking purpose. The classifier used in the proposed technique is Support Vector Machine (SVM). The experiment is performed on lymphoma data set and the result shows the better accuracy of classification when compared to the conventional method.
References
- Rui Xu, Anagnostopoulos, G.C. and Wunsch, D.C.I.I., "Multiclass Cancer Classification Using Semisupervised Ellipsoid ARTMAP and Particle Swarm Optimization with Gene Expression Data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.4, No.1, Pp. 65-77, 2007.
- Huilin Xiong and Xue-Wen Chen, "Optimized Kernel Machines for Cancer Classification Using Gene Expression Data", Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Pp. 1-7, 2005.
- Xiyi Hang, "Cancer Classification by Sparse Representation using Microarray Gene Expression Data", IEEE International Conference on Bioinformatics and Biomeidcine Workshops, Pp. 174-177, 2008.
- Runxuan Zhang, Huang, G.B., Sundararajan, N. and Saratchandran, P., "Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 4, No.3, Pp. 485 – 495, 2007.
- Brown, M., "Knowledge Based Analysis of Micorarray Gene Expression Data by using Support Vector Machines", In Proc. of the National Academy of Sciences, Vol. 97, Pp. 262–267, 2000.
- Xiaogang Ruan, Jinlian Wang, Hui Li and Xiaoming Li, "A Method for Cancer Classification Using Ensemble Neural Networks with Gene Expression Profile", The 2nd International Conference on Bioinformatics and Biomedical Engineering, Pp. 342-346, 2008.
- Berns, A., “Cancer: Gene Expression in Diagnosis”, Nature, Pp. 491–492, 2000.
- Alizadeh, A., “Distinct Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling”, Nature, Pp. 503–511, 2000.
- Campbell, V., Li, Y. and Tipping, N. “An Efficient Feature Selection Algorithm for Classification of Gene Expression Data”, 2001.
- Dubitzky, W., Granzow, M. and Berrar, D., “Comparing Symbolic and Subsymbolic Machine Learning Approaches to Classification of Cancer and Gene Indentification”, Kluwer Academic, 2002.
- Furey, T., Cristianini, N., Duffy, N., Bednarski, D., Schummer, M. and Haussler, D. “Support Vector Machine Classification and Validation of Cancer Tissue Samples using Microarray Expression Data”, Bioinformatics, 2001.
- Fujarewicz, K., Kimmel, M. and Rzeszowska-Wolny, J., “Improved Classification of Gene Expression Data using Support Vector Machines”, Journal of Medical Informatics and Technologies, Vol. 6, Nov 2001.
- Guyon, I., Weston, J., Barnhill, S. and Vapnik, V., “Gene Selection for Cancer Classification using Support Vector Machines”, Machine Learning, 2000.
- Khan, J., Wei, J., Ringner, M. and Saal, L., “Classification and Diagnostic Prediction of Cancers using Gene Expression Profiling and Artificial Neural Networks”, Nature Medicine, 2001.
- Ramaswamy, S., Tamayo, P. and Rifkin, R., “Multiclass Cancer Diagnosis using Tumor Gene Expression Signatures”, PNAS, Pp. 15149–15154, 2001.
- Zhang, H., Yu, C., Singer, B. and Xiong, M., “Recursive Partitioning for Tumor Classification with Gene Expression Microarray Data”. PNAS, Pp. 6730–6735, 2001.
Index Terms
Keywords