Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Sequence - Evolution - Function: Computational Approaches in Comparative Genomics
Sequence - Evolution - Function: Computational Approaches in Comparative Genomics
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Protein function prediction via graph kernels
Bioinformatics
A structural alignment kernel for protein structures
Bioinformatics
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Sequence and structure are complementary pieces of information that can be used to infer protein function. We study and compare sequence, structure and sequence-structure integrative kernels to recognize proteins with enzymatic function. Using a support-vector machine, we show that kernels that combine sequence and structure information typically perform better (AUC 0.73) at this task than kernels that exploit either type of information exclusively. We find that the feature space of structure kernels complements that of sequence kernels, making both sources of similarity more accessible to kernel methods.