Abstract
Gene expressions have been commonly applied to understand the inherent underlying mechanism of known biological processes. Although the microarray gene expressions usually appear aperiodic, with proper signal processing techniques, its periodic components can be easily obtained. Thus, if expressions of interconnected (regulatory and regulated) genes are decomposed, at least one common frequency component will appear in these genes. Exploiting this novel concept, we propose a frequency decomposition approach for gene clustering to better understand the gene interconnection topology. This method, based on Hilbert Huang Transform (HHT) enables us to segregate every periodic component of the gene expressions. Next, a multilevel clustering is performed based on these frequency components. Unlike existing clustering algorithms, the proposed method assimilates a meaningful knowledge of the gene interactions topology. The information related to underlying gene interactions is vital and can prove useful in many existing evolutionary optimisation algorithms for genetic network reconstruction. We validate the entire approach by its application to a 15-gene synthetic network.
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Acknowledgment
This research is supported by the Australia-India strategic research fund (AISRF) having the reference number BF040037.
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Rahman, M.A., Chetty, M., Bulach, D., Wangikar, P.P. (2015). Frequency Decomposition Based Gene Clustering. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_20
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DOI: https://doi.org/10.1007/978-3-319-26535-3_20
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