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Immunological Computation for Protein Function Prediction

Zekri M., Alem K., Souici-Meslati L.

Fundamenta Informaticae

2015

Vol. 139, nr 1

91--114

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.

Protein structural classification based on pseudo amino acid composition using SVM classifier

Krajewski Z., Tkacz E.

Biocybernetics and Biomedical Engineering

2013

Vol. 33, no. 2

77--87

This paper deals with a structural classification by the aid of support vector machine (SVM) classifier. Amino acid composition (AAC) and pseudo amino acid composition (PseAA) features were applied with different variants. Additionally the feature reflecting the length of protein chain was taken into consideration. The SVM classifier was compared to minimallength classifiers with respect to the AAC features. The best model of SVM classifier was chosen using grid method on the basis of cross-validation (CV) as criterion. The best model of SVM classifier is evaluated with respect to proper evaluation rates. The SCOP database and the ASTRAL tool were a source of non-homologous data to avoid the redundancy and to ensure a maximal amount of available data.

Feature selection of protein structural classification using SVM classifier

Krajewski Z., Tkacz E.

Biocybernetics and Biomedical Engineering

2013

Vol. 33, no. 1

47--61

Recursive feature elimination method (RFE), cross validation coefficient (CV) and accuracy of classification of test data are applied as a criterion of feature selection in order to find relevant features and to analyze their influence on classifier accuracy. Feature selection method was compared to principal component analysis (PCA) to understand the effectiveness of feature reduction. Support vector machine classifier with radial basis function (RBF) kernel is applied to find the best set of features using grid model selection and to select and assess relevant features. The best selected feature set is then analyzed and interpreted as the source of knowledge about the protein structure and biochemical properties of amino acids included in the protein domain sequence.