Abstract
The ability of proteomics in detecting particular disease in the early stages intrigues researchers, especially analytical researchers, computer scientists and mathematicians. Further, high throughput of proteomics pattern derived from mass spectrometry analysis has embarked new paradigm for biomarker analysis through accessible body fluids such as serum, saliva, and urine. Recently, sophisticated computational techniques that are mimetic natural survival and behaviour of organisms have been widely adopted in problem-solving algorithm. As we put emphasis on feature selection algorithm, the most challenging phase in biomarker analysis is selecting most parsimonious features of voluminous mass spectrometry data. Therefore this study reveals the hybrid artificial bee colony and differential evolution as feature selection techniques exhibits comparable results. These results were compared with other types of bio-inspired algorithms such as ant colony and particle swarm optimisation. The proposed method produced; 1) 100 percent and 98.44 of accuracy of the ovarian cancer dataset; and 2) 100 percent and 94.44 percent for TOX dataset for both training and testing respectively.
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Yusoff, S.A.M., Abdullah, R., Venkat, I. (2014). Adapted Bio-inspired Artificial Bee Colony and Differential Evolution for Feature Selection in Biomarker Discovery Analysis. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_11
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DOI: https://doi.org/10.1007/978-3-319-07692-8_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07691-1
Online ISBN: 978-3-319-07692-8
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