K-Means Clustering with Infinite Feature Selection for Classification Tasks in Gene Expression Data

• Autores: Muhammad Akmal Remli, Kauthar Mohd Daud, Hui Wen Nies, Mohd Saberi Mohamad, Safaai Deris, Sigeru Omatu , Shahreen Kasim, Ghazali Sulong
• Localización: 11th International Conference on Practical Applications of Computational Biology & Bioinformatics / Florentino Fernández Riverola (ed. lit.) , 2017, ISBN 978-3-319-60815-0, págs. 50-57
• Idioma: inglés
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• Resumen

  • In the bioinformatics and clinical research areas, microarray tech‐ nology 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. There‐ fore, 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 infor‐ mative 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.