Support Vector Machines for Predicting Membrane Protein Types by Using Functional Domain Composition
2003
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Details
- Autor(en) / Beteiligte
- Titel
- Support Vector Machines for Predicting Membrane Protein Types by Using Functional Domain Composition
- Ist Teil von
- Biophysical journal, 2003-05, Vol.84 (5), p.3257-3263
- Ort / Verlag
- United States: Elsevier Inc
- Erscheinungsjahr
- 2003
- Quelle
- MEDLINE
- Beschreibungen/Notizen
- Membrane proteins are generally classified into the following five types: 1), type I membrane protein; 2), type II membrane protein; 3), multipass transmembrane proteins; 4), lipid chain-anchored membrane proteins; and 5), GPI-anchored membrane proteins. In this article, based on the concept of using the functional domain composition to define a protein, the Support Vector Machine algorithm is developed for predicting the membrane protein type. High success rates are obtained by both the self-consistency and jackknife tests. The current approach, complemented with the powerful covariant discriminant algorithm based on the pseudo-amino acid composition that has incorporated quasi-sequence-order effect as recently proposed by K. C. Chou (2001), may become a very useful high-throughput tool in the area of bioinformatics and proteomics.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0006-3495eISSN: 1542-0086DOI: 10.1016/S0006-3495(03)70050-2Titel-ID: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1302886
- Format
- –
- Schlagworte
- Algorithms, Amino Acid Sequence, Amino acids, Cells, Computing Methodologies, Discriminant Analysis, Membrane Proteins - chemistry, Membrane Proteins - classification, Molecular Sequence Data, Protein Conformation, Protein Structure, Tertiary, Proteins, Reproducibility of Results, Sensitivity and Specificity, Sequence Analysis, Protein - methods