@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}, }