Title :
A multi-fold string kernel for sequence classification
Author :
Aniruddha Maiti;Santanu Ghorai;Anirban Mukherjee
Author_Institution :
Aniruddha Maiti is with the Department of Computer and Information Sciences, Temple University, PA, USA
Abstract :
A novel framework is proposed to classify biological sequences using a kernel. It considers the topological information along with the primary structural information. The widely used string kernel for sequence classification does not take into account the structural information which might be available for biological sequences. The proposed kernels incorporate the additional structural information and thus make the features more informative.
Keywords :
"Kernel","Accuracy","Proteins","Support vector machines","Feature extraction","Hidden Markov models"
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
Electronic_ISBN :
1558-4615
DOI :
10.1109/EMBC.2015.7319874
