Abstract:
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...Show MoreMetadata
Abstract:
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.
Published in: 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
Date of Conference: 30 March 2009 - 02 April 2009
Date Added to IEEE Xplore: 15 May 2009
Print ISBN:978-1-4244-2756-7