Title :
PROCANS: a protein classification system using a neural network
Author :
Wu, Cathy H. ; Whitson, George M. ; Montllor, Gregory J.
Abstract :
An approach to the prediction of protein structure/function from primary sequence that is a direct protein/domain classification using a neural network is presented. The protein classification system, PROCANS, is able to classify unknown proteins to protein/domain classes by imbedding a domain database in a back-propagation network. The initial results of using PROCANS for 30 training records are discussed. The system converged in 89 Cray CPU seconds during training, and the classification was rapid and accurate for all unknown cases tested. The algorithm differs from many other methods in that it does not require an exhaustive database search or a primary sequence alignment for sequence comparisons. These features make it possible to scale-up the system to embed the entire domain database and classify at least 1000 protein/domain classes
Keywords :
biology computing; classification; deductive databases; macromolecular configurations; molecular biophysics; neural nets; physics computing; proteins; Cray CPU; PROCANS; back-propagation network; domain database; neural network; protein classification system; protein function; protein structure;
Conference_Titel :
Neural Networks, 1990., 1990 IJCNN International Joint Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/IJCNN.1990.137700