Abstract
Microarray technology provides an enormous opportunity to measure large-scale gene expressions simultaneously. However sparse and high-dimensional feature space posed significant challenges during data analysis, mainly in learning network structure. Hence, feature selection (FS) has become an essential phase in microarray data analysis to obtain significant genes that could enhance the performance of subsequent process. This study aims to propose a hybrid FS methods based on Binary Bat Algorithm (BBA) and Dynamic Bayesian Network (DBN). The proposed method is tested on cancer gene expression dataset that is publicly available. The fold change analysis is conducted to measure a gene expression level between two diverse conditions prior to subsequent process of FS. Experimental results show that BBA has achieved better results compared to other baseline methods when trained with DBN low-order conditional independence and DBN full-order conditional independence with accuracy of 89.1% and 83.3% respectively. Additionally, several informative genes are identified that regulates cancer proliferation.
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Ahmad, F.K., Kamaruddin, S.S., Tuaimah, A.T.N. (2022). Binary Bat Algorithm with Dynamic Bayesian Network for Feature Selection on Cancer Gene Expression Profiles. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_15
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DOI: https://doi.org/10.1007/978-3-031-00828-3_15
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