title: A multi-fold string kernel for sequence classification.
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.
authors.forename: Aniruddha
authors.initials: A
authors.lastname: Maiti
authors.forename: Santanu
authors.initials: S
authors.lastname: Ghorai
authors.forename: Anirban
authors.initials: A
authors.lastname: Mukherjee
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001331:Automation; D001665:Binding Sites; D030562:Databases, Protein; D006801:Humans; D008024:Ligands; D008390:Markov Chains; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry; D011506:Proteins / Q000737:chemistry*; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
chemical_list: D008024:Ligands; D010455:Peptides; D011506:Proteins; D043562:Receptors, G-Protein-Coupled