Abstract
Computational approaches have been applied in many different biology application domains. When such tools are based on conventional computation, their approach has shown limitations when dealing with complex biological problems. In the present study, a computational evolutionary environment (GASNP) is proposed as a tool to extract classification rules from biological dataset. The main goal of the proposed approach is to allow the discovery of concise, and accurate, high-level rules (from a biological database named dbSNP - Database Single Nucleotide Polymorphism) which can be used as a classification system. More than focusing only on the classification accuracy, the proposed GASNP model aims at balancing prediction precision, interpretability and comprehensibility. The obtained results show that the proposed GASNP has great potential and is capable of extracting useful high-level knowledge that could not be extracted by traditional classification methods such as Decision Trees, One R and the Single Conjunctive Rule Learner, among others, using the same dataset.
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Bevilaqua, A., Rodrigues, F.A., do Amaral, L.R. (2011). SNPs Classification: Building Biological High-Level Knowledge Using Genetic Algorithms. In: Hruschka, E.R., Watada, J., do Carmo Nicoletti, M. (eds) Integrated Computing Technology. INTECH 2011. Communications in Computer and Information Science, vol 165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22247-4_5
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DOI: https://doi.org/10.1007/978-3-642-22247-4_5
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