Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Rational Kernels: Theory and Algorithms
The Journal of Machine Learning Research
Profile-Based String Kernels for Remote Homology Detection and Motif Extraction
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Fast String Kernels using Inexact Matching for Protein Sequences
The Journal of Machine Learning Research
Semi-supervised protein classification using cluster kernels
Bioinformatics
Large scale genomic sequence SVM classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Multi-class Protein Classification Using Adaptive Codes
The Journal of Machine Learning Research
Spatial Representation for Efficient Sequence Classification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Classifying Proteins by Amino Acid Variations of Sequential Patterns
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Characterizing Amino Acid Variations of Scavenger Receptors by Class Information Gain
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on tasks such as document topic elucidation, biological sequence classification, or protein superfamily and fold prediction. However, typical string kernel methods rely on analysis of discrete 1D string data (e.g., DNA or amino acid sequences). This work introduces new 2D kernel methods for sequence data in the form of sequences of feature vectors (as in biological sequence profiles, or sequences of individual amino acid physico-chemical descriptors). On three protein sequence classification tasks proposed 2D kernels show significant 15-20% improvements compared to state-of-the-art sequence classification methods.