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Relevant and Non-Redundant Amino Acid Sequence Selection for Protein Functional Site Identification

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.