Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Automated data-driven discovery of motif-based protein function classifiers
Information Sciences: an International Journal
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IBM Systems Journal - Deep computing for the life sciences
New techniques for extracting features from protein sequences
IBM Systems Journal - Deep computing for the life sciences
Motif Extraction and Protein Classification
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Software note: ECS: An automatic enzyme classifier based on functional domain composition
Computational Biology and Chemistry
| Hi-index | 0.00 |
Automated classification of proteins is indispensable for further in vivo investigation of excessive number of unknown sequences generated by large scale molecular biology techniques. This study describes a discriminative system based on feature space mapping, called subsequence profile map (SPMap) for functional classification of protein sequences. SPMap takes into account the information coming from the subsequences of a protein. A group of protein sequences that belong to the same level of classification is decomposed into fixed-length subsequences and they are clustered to obtain a representative feature space mapping. Mapping is defined as the distribution of the subsequences of a protein sequence over these clusters. The resulting feature space representation is used to train discriminative classifiers for functional families. The aim of this approach is to incorporate information coming from important subregions that are conserved over a family of proteins while avoiding the difficult task of explicit motif identification. The performance of the method was assessed through tests on various protein classification tasks. Our results showed that SPMap is capable of high accuracy classification in most of these tasks. Furthermore SPMap is fast and scalable enough to handle large datasets.