Classification Neural Networks for Rapid Sequence Annotation and Automated Database Organization

Abstract A neural network classification method has been developed as an alternative approach to the search/organization problem of large molecular 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 back-propagation networks for classification. The protein system, which classifies proteins into PIR (Protein Identification Resource) superfamilies, has achieved 82–100% sensitivity at a speed that is about an order of magnitude faster than other search methods. The pilot nucleic acid system showed a 91–97% classification accuracy. The software tool could be used as a filter program to reduce the database search time and help organize the molecular sequence databases. The tool is generally applicable to any databases that are organized according to family relationships.

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