Predicting Enzyme Subclasses by Using Support Vector Machine with Composite Vectors
Based on enzyme sequence, using composite vectors with amino acid composition, low-frequency power spectral density, increment of diversity by combining a different form of pseudo amino acid composition to express the information of sequence, a support vector machine (SVM) for predicting enzyme subclasses is proposed. By the jackknife test, success rates of our algorithm are higher than other methods.
No Supplementary Data
Document Type: Research Article
Publication date: 2010-05-01
More about this publication?
- Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.