ReviewFrontiers in cellular and infection microbiology2021
Lessons Learnt From Using the Machine Learning Random Forest Algorithm to Predict Virulence in
Review in Frontiers in cellular and infection microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Artificial intelligence strategies based on random forests for detecting ischemia-reperfusion injury changes in kidney tissue during intravital imaging.Scientific reports · 2025Article
- Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics.Bioinformatics (Oxford, England) · 2025Article
- Characteristic genes and immune infiltration analysis of gastric cancer based on bioinformatics analysis and machine learning.Discover oncology · 2025Article
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Article
- Disulfidptosis-related Protein RPN1 may be a Novel Anti-osteoporosis Target of Kaempferol.Combinatorial chemistry & high throughput screening · 2024Article
- Identification of ferroptosis-associated biomarkers for the potential diagnosis and treatment of postmenopausal osteoporosis.Frontiers in endocrinology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Group A
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.