ArticleJournal of clinical microbiology2012
Prediction system for rapid identification of Salmonella serotypes based on pulsed-field gel electrophoresis fingerprints.
Article in Journal of clinical microbiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of pulsed-field gel electrophoresis fingerprints based on a constructed Salmonella database.PloS one · 2013Pooled it
- Evaluation ofFrontiers in microbiology · 2021Article
- CSESA: an R package to predict Salmonella enterica serotype based on newly incorporated spacer pairs of CRISPR.BMC bioinformatics · 2019Article
- A novel procedure on next generation sequencing data analysis using text mining algorithm.BMC bioinformatics · 2016Article
- Article
- Topic modeling for cluster analysis of large biological and medical datasets.BMC bioinformatics · 2014Article
- Longitudinal study of distributions of similar antimicrobial-resistant Salmonella serovars in pigs and their environment in two distinct swine production systems.Applied and environmental microbiology · 2013Article
- Prediction of the origin of French Legionella pneumophila strains using a mixed-genome microarray.BMC genomics · 2013Article
- Comparison of typing methods with a new procedure based on sequence characterization for Salmonella serovar prediction.Journal of clinical microbiology · 2013Article
- Data mining tools for Salmonella characterization: application to gel-based fingerprinting analysis.BMC bioinformatics · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
A classification model is presented for rapid identification of Salmonella serotypes based on pulsed-field gel electrophoresis (PFGE) fingerprints. The classification model was developed using random forest and support vector machine algorithms and was then applied to a database of 45,923 PFGE patterns, randomly selected from all submissions to CDC PulseNet from 2005 to 2010. The patterns selected included the top 20 most frequent serotypes and 12 less frequent serotypes from various sources. The prediction accuracies for the 32 serotypes ranged from 68.8% to 99.9%, with an overall accuracy of 96.0% for the random forest classification, and ranged from 67.8% to 100.0%, with an overall accuracy of 96.1% for the support vector machine classification. The prediction system improves reliability and accuracy and provides a new tool for early and fast screening and source tracking of outbreak isolates. It is especially useful to get serotype information before the conventional methods are done. Additionally, this system also works well for isolates that are serotyped as "unknown" by conventional methods, and it is useful for a laboratory where standard serotyping is not available.
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Registered trials
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