ArticleFrontiers in microbiology2023
Development and validation of a random forest algorithm for source attribution of animal and human
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Impact of Farm Management Practices onFoods (Basel, Switzerland) · 2026Review
- Predicting clinical outcome ofMicrobial genomics · 2025Article
- Development and validation of machine learning models for distant metastasis of primary hepatic carcinoma: a population-based study.Discover oncology · 2025Article
- Comparison of three source attribution methods applied to whole genome sequencing data of monophasic and biphasicFrontiers in microbiology · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical
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Registered trials
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