Abstract
The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.
Keywords
Support vector machineTopology (electrical circuits)False positive paradoxComputer scienceMembrane topologyTransmembrane proteinComputational biologySignal peptideMembrane proteinData miningProtein Sorting SignalsSource codeAlgorithmArtificial intelligenceBioinformaticsPattern recognition (psychology)Transmembrane domainPeptide sequenceBiologyMathematicsGeneGenetics
MeSH Terms
AlgorithmsAmino Acid SequenceArtificial IntelligenceMembrane ProteinsModelsStatisticalPattern RecognitionAutomatedProtein FoldingProtein Sorting SignalsProtein StructureSecondaryProteomicsReproducibility of Results
Affiliated Institutions
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Publication Info
- Year
- 2009
- Type
- article
- Volume
- 10
- Issue
- 1
- Pages
- 159-159
- Citations
- 432
- Access
- Closed
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Cite This
Timothy Nugent,
David T. Jones
(2009).
Transmembrane protein topology prediction using support vector machines.
BMC Bioinformatics
, 10
(1)
, 159-159.
https://doi.org/10.1186/1471-2105-10-159
Identifiers
- DOI
- 10.1186/1471-2105-10-159
- PMID
- 19470175
- PMCID
- PMC2700806