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Efficient Design of Bio-Basis Function to Predict Protein Functional Sites Using Kernel-Based Classifiers
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DOI：10.1109/TNB.2010.2080684.png)
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In order to apply the powerful kernel-based pattern recognition algorithms such as support vector machines to predict functional sites in proteins, amino acids need encoding prior to input. In this regard, a new string kernel function, termed as the modified bio-basis function, is proposed that maps a nonnumerical sequence space to a numerical feature space. The proposed string kernel function is developed based on the conventional bio-basis function and needs a bio-basis string as a support like conventional kernel function. The concept of zone of influence of a bio-basis string is introduced in the proposed kernel function to take into account the influence of each bio-basis string in nonnumerical sequence space. An efficient method is described to select a set of bio-basis strings for the proposed kernel function, integrating the Fisher ratio and a novel concept of degree of resemblance. The integration enables the method to select a reduced set of relevant and nonredundant bio-basis strings.
Keyword:
Bioinformatics
functional site prediction
pattern recognition
sequence analysis
support vector machines
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4.4
论文数:
1.4K
被引数:
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