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The prediction of functional sites in proteins is an important issue in protein function studies and drug design. To apply the kernel based pattern recognition algorithms such as support vector machines for protein functional sites prediction, a new string kernel function, termed as the modified bio-basis function, is proposed recently. The bio-basis strings for the new kernel function are selected by an efficient method that integrates the Fisher ratio and the concept of degree of resemblance. In this regard, this paper introduces some quantitative indices for evaluating the quality of selected bio-basis strings. Moreover, the effectiveness of the new string kernel function and bio-basis string selection method, along with a comparison with existing bio-basis function and related bio-basis string selection methods, is demonstrated on different protein data sets using the proposed quantitative indices and support vector machines.