Abstract
Enzymes are important in our life and it plays a vital role in the most biological processes. Computational classification of the enzyme’s function is necessary to save efforts and time. In this paper, an information fusion-based approach is proposed. The unknown sequence is classified through aligning it with all labelled sequences using local pairwise sequence alignment based on different score matrices. The outputs of all pairwise sequence alignment processes are represented by a set of scores. The scores of alignment processes are combined using simple fusion rules. The results of the fusion-based approach achieved results better than all individual sequence alignment processes.
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Tharwat, A., Sharif, M.M., Hassanien, A.E., Hefeny, H.A. (2015). Improving Enzyme Function Classification Performance Based on Score Fusion Method. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_44
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DOI: https://doi.org/10.1007/978-3-319-19644-2_44
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