  The subcellular location of a protein can provide valuable information about
its function. With the rapid increase of sequenced genomic data, the need for
an automated and accurate tool to predict subcellular localization becomes
increasingly important. Many efforts have been made to predict protein
subcellular localization. This paper aims to merge the artificial neural
networks and bioinformatics to predict the location of protein in yeast genome.
We introduce a new subcellular prediction method based on a backpropagation
neural network. The results show that the prediction within an error limit of 5
to 10 percentage can be achieved with the system.
