Efficient design of bio-basis function to predict protein functional sites using kernel-based classifiers.
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.
Maji
Pradipta
P
Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700 108, India. pmaji@isical.ac.in
Das
Chandra
C
D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology; D016247:Information Storage and Retrieval / Q000379:methods*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011485:Protein Binding; D020539:Sequence Analysis, Protein / Q000379:methods*