Using neural networks for prediction of subcellular location of prokaryotic and eukaryotic proteins.
T. Kohonen's self-organization model, a typical neural network model, was applied to predict the subcellular location of proteins from their amino acid composition. The Reinhardt and Hubbard database was used to examine the performance of the neural network method. The rates of correct prediction for the three possible subcellular location of prokaryotic proteins were 96.1% by the self-consistency test and 84.4% by the jackknife test. The rates of correct prediction for the four possible subcellular location of eukaryotic proteins were 95.6% by the self-consistency test and 70.6% by the jackknife test.
Cai
Y D
YD
Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai, 200233, China. yuc@aber.ac.uk
Chou
K C
KC
D000465:Algorithms; D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000032:analysis*; D016208:Databases, Factual; D016571:Neural Networks, Computer; D011506:Proteins / Q000032:analysis*
D001426:Bacterial Proteins; D011506:Proteins