Classification artificial neural systems for genome research

A neural network classification method has been developed as an alternative approach to the search/organization problem of large modular databases. Two artificial neural systems have been implemented on a Cray for rapid protein/nucleic acid classification of unknown sequences. The system employs a n-gram hashing function for sequence encoding and modular backpropagation networks for classification. The protein system has achieved a 82 to 100% sensitivity at a speed that is about an order of magnitude faster than other search methods. With the rapid accumulation of sequences, the saving in time will become increasingly significant. The pilot nucleic acid system showed a 96% classification accuracy. The software tool would be valuable for the organization of molecular sequence databases and is generally applicable to any databases that are organized according to family relationships.< >