The nature of statistical learning theory
The nature of statistical learning theory
Artificial neural network model for predicting HIV protease cleavage sites in protein
Advances in Engineering Software
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Matching Protein b-Sheet Partners by Feedforward and Recurrent Neural Networks
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Paradigms of Denotational Mathematics for Cognitive Informatics and Cognitive Computing
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
Contemporary cybernetics and its facets of cognitive informatics and computational intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Editorial Recent Advances in Cognitive Informatics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Cognitive informatics models of the brain
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Orthogonal kernel Machine for the prediction of functional sites in proteins
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bio-basis function neural network for prediction of protease cleavage sites in proteins
IEEE Transactions on Neural Networks
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In order to apply a powerful pattern recognition algorithm to predict functional sites in proteins, amino acids cannot be used directly as inputs since they are non-numerical variables. Therefore, they need encoding prior to input. In this regard, the bio-basis function maps a non-numerical sequence space to a numerical feature space. One of the important issues for the bio-basis function is how to select a minimum set of bio-basis strings with maximum information. In this paper, an efficient method to select bio-basis strings for the bio-basis function is described integrating the concepts of the Fisher ratio and "degree of resemblance". The integration enables the method to select a minimum set of most informative bio-basis strings. The "degree of resemblance" enables efficient selection of a set of distinct bio-basis strings. In effect, it reduces the redundant features in numerical feature space. Quantitative indices are proposed for evaluating the quality of selected bio-basis strings. The effectiveness of the proposed bio-basis string selection method, along with a comparison with existing methods, is demonstrated on different data sets.