ReviewGenome biology2025
Machine learning and statistical inference in microbial population genomics.
Review in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Genomic signatures of host adaptation in Pasteurella multocida from poultry, cattle, swine, and cats: insights from interpretable machine learning.Poultry science · 2026Article
- Genome-based source attribution using a One HealthMicrobial genomics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The availability of large genome datasets has changed the microbiology research landscape. Analyzing such data requires computationally demanding analyses, and new approaches have come from different data analysis philosophies. Machine learning and statistical inference have overlapping knowledge discovery aims and approaches. However, machine learning focuses on optimizing prediction, whereas statistical inference focuses on understanding the processes relating variables. In this review, we outline the different aspirations, precepts, and resulting methodologies, with examples from microbial genomics. Emphasizing complementarity, we argue that the combination and synthesis of machine learning and statistics has potential for pathogen research in the big data era.
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.