Abstract
Neural networks have been trained to predict the subcellular location of proteins in prokaryotic or eukaryotic cells from their amino acid composition. For three possible subcellular locations in prokaryotic organisms a prediction accuracy of 81% can be achieved. Assigning a reliability index, 33% of the predictions can be made with an accuracy of 91%. For eukaryotic proteins (excluding plant sequences) an overall prediction accuracy of 66% for four locations was achieved, with 33% of the sequences being predicted with an accuracy of 82% or better. With the subcellular location restricting a protein's possible function, this method should be a useful tool for the systematic analysis of genome data and is available via a server on the world wide web.
Keywords
BiologySubcellular localizationComputational biologyGenomeProtein subcellular localization predictionFunction (biology)Artificial neural networkReliability (semiconductor)Artificial intelligenceGeneticsComputer scienceGene
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Publication Info
- Year
- 1998
- Type
- article
- Volume
- 26
- Issue
- 9
- Pages
- 2230-2236
- Citations
- 588
- Access
- Closed
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Cite This
Anita Reinhardt
(1998).
Using neural networks for prediction of the subcellular location of proteins.
Nucleic Acids Research
, 26
(9)
, 2230-2236.
https://doi.org/10.1093/nar/26.9.2230
Identifiers
- DOI
- 10.1093/nar/26.9.2230