@article{(Open Science Index):https://publications.waset.org/pdf/283,
	  title     = {Gene Expression Signature for Classification of Metastasis Positive and Negative Oral Cancer in Homosapiens},
	  author    = {A. Shukla and  A. Tarsauliya and  R. Tiwari and  S. Sharma},
	  country	= {},
	  institution	= {},
	  abstract     = {Cancer classification to their corresponding cohorts has been key area of research in bioinformatics aiming better prognosis of the disease. High dimensionality of gene data has been makes it a complex task and requires significance data identification technique in order to reducing the dimensionality and identification of significant information. In this paper, we have proposed a novel approach for classification of oral cancer into metastasis positive and negative patients. We have used significance analysis of microarrays (SAM) for identifying significant genes which constitutes gene signature. 3 different gene signatures were identified using SAM from 3 different combination of training datasets and their classification accuracy was calculated on corresponding testing datasets using k-Nearest Neighbour (kNN), Fuzzy C-Means Clustering (FCM), Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN). A final gene signature of only 9 genes was obtained from above 3 individual gene signatures. 9 gene signature-s classification capability was compared using same classifiers on same testing datasets. Results obtained from experimentation shows that 9 gene signature classified all samples in testing dataset accurately while individual genes could not classify all accurately.
},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {6},
	  number    = {11},
	  year      = {2012},
	  pages     = {611 - 617},
	  ee        = {https://publications.waset.org/pdf/283},
	  url   	= {https://publications.waset.org/vol/71},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 71, 2012},
	}