ArticleFrontiers in pharmacology2025
Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-
Article in Frontiers in pharmacology, 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.
- Disruption of thePathogens (Basel, Switzerland) · 2026Article
- Elucidating key targets and mechanisms of diethyl phthalate-induced colorectal cancer through network toxicology and molecular docking.PloS one · 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
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Candidiasis, mainly caused by Methods: We evaluated 12 machine learning models on a self-constructed dataset with known anti- Result: Among the evaluated machine learning models, the best predictive model was an ensemble learning model constructed from Random Forests and Categorical Boosting using soft voting. It predicts that Dp44mT exhibits potent anti- Conclusion: This study presents a practical approach for predicting the antifungal activity of com-pounds using machine learning models and provides new insights into the development of antifungal compounds by disrupting iron homeostasis in
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.