| Abstract: | A computer program was developed to identify anaerobic bacteria by using simultaneous pattern recognition via a Bayesian probabilistic model. The system is intended for use as a rapid, precise, and reproducible aid in the identification of unknown isolates. The program operates on a data base of 28 genera comprising 238 species of anaerobic bacteria that can be separated by the program. Input to the program consists of biochemical and gas chromatographic test results in binary format. The system is flexible and yields outputs of: (i) most probable species, (ii) significant test results conflicting with established data, and (iii) differential tests of significance for missing test results. |
