Abstract
In the bioinformatics and clinical research areas, microarray technology has been widely used to distinguish a cancer dataset between normal and tumour samples. However, the high dimensionality of gene expression data affects the classification accuracy of an experiment. Thus, feature selection is needed to select informative genes and remove non-informative genes. Some of the feature selection methods, yet, ignore the interaction between genes. Therefore, the similar genes are clustered together and dissimilar genes are clustered in other groups. Hence, to provide a higher classification accuracy, this research proposed k-means clustering and infinite feature selection for identifying informative genes in the selected subset. This research has been applied to colorectal cancer and small round blue cell tumors datasets. Eventually, this research successfully obtained higher classification accuracy than the previous work.
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Acknowledgements
We would like to thank Universiti Teknologi Malaysia for funding this research through GUP Research Grants (grant numbers: Q.J130000.2528.12H12 and Q.J130000.2528.11H05). This research is also funded by Malaysian Ministry of Higher Education under a fundamental research grant (grant number: 1559).
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Remli, M.A. et al. (2017). K-Means Clustering with Infinite Feature Selection for Classification Tasks in Gene Expression Data. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_7
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