title: Using neural networks for prediction of the subcellular location of proteins.
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.
authors.affiliation: The Sanger Centre, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. areinha@sanger.ac.uk
authors.forename: A
authors.initials: A
authors.lastname: Reinhardt
authors.forename: T
authors.initials: T
authors.lastname: Hubbard
mesh_terms: D000596:Amino Acids; D002451:Cell Compartmentation; D003195:Computer Communication Networks; D016208:Databases, Factual; D005057:Eukaryotic Cells; D005544:Forecasting; D016571:Neural Networks, Computer; D011387:Prokaryotic Cells; D011506:Proteins / Q000737:chemistry; D015203:Reproducibility of Results
chemical_list: D000596:Amino Acids; D011506:Proteins