Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Computer-assisted identification of anaerobic bacteria.

R W Kelley, S T Kellogg

    Applied and Environmental Microbiology
    |March 1, 1978
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Evidence supporting the interaction of CYP2B4 and CYP1A2 in microsomal preparations.

    Drug metabolism and disposition: the biological fate of chemicals·2001
    Same author

    Characterization of bacterial communities in heavy metal contaminated soils.

    Canadian journal of microbiology·1996
    Same author

    Comparison of media for isolation of poultry intestinal bacteria.

    Applied and environmental microbiology·1983
    Same author

    Biodegradation of 2,4,5-trichlorophenoxyacetic acid by a pure culture of Pseudomonas cepacia.

    Applied and environmental microbiology·1982
    Same author

    Phenotypic differentiation of some of the Veillonella species with the API ZYM system.

    Canadian journal of microbiology·1982
    Same author

    Plasmid-assisted molecular breeding: new technique for enhanced biodegradation of persistent toxic chemicals.

    Science (New York, N.Y.)·1981
    Same journal

    Assessing the effects of ocean alkalinity enhancement on marine protozoa: physiological dynamics and transcriptomic responses.

    Applied and environmental microbiology·2026
    Same journal

    The <i>Streptococcus mutans</i> collagen-binding protein Cnm enhances early biofilm formation with <i>Candida albicans</i>.

    Applied and environmental microbiology·2026
    Same journal

    Response of <i>Zostera japonica</i> rhizosphere bacteria to ocean acidification.

    Applied and environmental microbiology·2026
    Same journal

    Dynamics of clinical <i>Klebsiella pneumoniae</i> strains over the COVID-19 pandemic in Qingdao, China.

    Applied and environmental microbiology·2026
    Same journal

    A set of constitutive promoters with graded strengths for gene expression in diverse cyanobacterial strains.

    Applied and environmental microbiology·2026
    Same journal

    Sources and traits of bacteria and fungi found in the near-surface atmosphere.

    Applied and environmental microbiology·2026
    See all related articles

    A new computer program uses a Bayesian probabilistic model for rapid and precise identification of anaerobic bacteria. This tool aids in distinguishing between 238 species, improving diagnostic accuracy in clinical microbiology.

    Area of Science:

    • Microbiology
    • Computational Biology
    • Bioinformatics

    Background:

    • Accurate identification of anaerobic bacteria is crucial for effective clinical treatment.
    • Traditional identification methods can be time-consuming and may lack precision.
    • Development of automated systems can enhance laboratory efficiency.

    Purpose of the Study:

    • To develop a computer program for the identification of anaerobic bacteria.
    • To utilize a Bayesian probabilistic model for pattern recognition in bacterial identification.
    • To provide a rapid, precise, and reproducible aid for identifying unknown anaerobic bacterial isolates.

    Main Methods:

    • Development of a computer program employing a Bayesian probabilistic model.
    • Utilizing simultaneous pattern recognition for bacterial identification.

    Related Experiment Videos

  • Inputting biochemical and gas chromatographic test results in binary format.
  • Database includes 28 genera and 238 species of anaerobic bacteria.

    • Main Results:

    • The program successfully identifies anaerobic bacteria based on provided test results.
    • It offers outputs including the most probable species identification.
    • The system highlights conflicting test results and suggests differential tests for missing data.

    Conclusions:

    • The developed computer program serves as an effective tool for anaerobic bacteria identification.
    • The Bayesian probabilistic model enables rapid, precise, and reproducible results.
    • This system can significantly aid clinical microbiology laboratories in diagnosing infections.