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Immunological computation is one of the largest recent bio-inspired approaches of artificial intelligence. Artificial immune systems (AIS) are inspired by the processes of the biological immune systems like the learning and memory characteristics which are used for solving complex problems. During the last two decades, AIS have been applied in various fields such as optimization, network security and data mining. In this article, we focus on the application of AIS to data mining in bioinformatics, more specifically, the classification task. For this purpose, we suggest three immune models based on clonal selection theory for the identification of G-protein coupled receptors (GPCRs) to predict their function. Our three classifiers are the artificial immune recognition system (AIRS), the clonal selection algorithm (CLONALG) and the clonal selection classification algorithm (CSCA). The GPCRs represent one of the largest and most important families of multifunctional proteins and are a significant target for bioactive and drug discovery programs. It is estimated that more than half of the drugs on the market currently target GPCRs. However, although thousands of GPCRs sequences are known, many of them remain orphans, have unknown function. Our experiments show that the three immunological classifiers have provided interesting results, however, AIRS obtained the best ones. Therefore, it is, for us, the most suitable immune model for the GPCRs identification problem.
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Czasopismo
Rocznik
Tom
Strony
91--114
Opis fizyczny
Bibliogr. 79 poz., rys., tab.
Twórcy
autor
- LISCO Laboratory, LabGED Laboratory Department of Computer Science Badji Mokhtar–Annaba University, Annaba, Algeria
autor
- LBBM Laboratory, Department of Biochemistry Badji Mokhtar–Annaba University, Annaba, Algeria
autor
- LISCO Laboratory, Department of Computer Science Badji Mokhtar–Annaba University, Annaba, Algeria
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d4b088db-5dcd-4f73-b07a-dff30c901c10