Identification of bacterial rep-PCR genomic fingerprints using a backpropagation neural network

A backpropagation neural network (BPN) was used to identify bacterial plant pathogens based on their genomic fingerprints. Genomic fingerprint data, c omprised of complex DNA band patterns generated using BOX, enterobacterial repetitive intergenic consensus (ERIC) and repetitive extragenic palindromi c (REP)-primers (rep-PCR), were used to train three independent BPNs. 10 St rains of the genus Xanthomonas, each with a characteristic host plant range , were identified correctly using the three trained BPNs. When tested with fingerprints of bacterial strains not present in the training sets, the rej ection rate was 100%, using the three BPN classifiers combined. Thus, BPN p rotocols can be employed to generate a powerful computer-based system for t he identification of pathogenic bacteria in the genus Xanthomonas. (C) 1999 Federation of European Microbiological Societies. Published by Elsevier Sc ience B.V. All rights reserved.