ArticleJournal of microbiology & biology education2025
A case study of ChatGPT-assisted building of a microbiome-based machine learning model for biologists.
Article in Journal of microbiology & biology education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Who cites it
1 citing paper in PubMed.
- Truth, tone, and trust: integrating an AI exercise to foster responsible science communication in a graduate biomedical science course.Journal of microbiology & biology education · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning is a widespread technology that is shaping how biologists interact with data. However, there are many practical challenges in teaching machine learning to biology students, who often do not have a strong programming background. To address these challenges, we present an educational study utilizing publicly available salivary microbiome data sets to develop a machine learning model using Python. With the assistance of ChatGPT, most students successfully built a simple random forest model. Evaluation metrics, such as accuracy and area under the curve, indicated that the overall performance of the model was favorable and accurately predicted oral malodor diseases. This work establishes a pedagogical framework for integrating machine learning into biology curricula, bridging the gap between data science and life science education.
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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.