ArticleACS pharmacology & translational science2025
Integrating AI, Machine Learning, and Animal Models for Precision Oncology: Bridging Preclinical and Clinical Gaps.
Article in ACS pharmacology & translational science, 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.
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Animal models of idiopathic membranous nephropathy: Recent advances and future perspectives.Animal models and experimental medicine · 2026Review
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
The limited translatability of animal models can be significantly amplified by integration of Artificial Intelligence (AI) and Machine Learning (ML). This Viewpoint represents a fresh paradigm in pharmacology and translational science, one that accelerates hypothesis testing, reduces resource burden, and improves clinical predictability. By aligning computational precision with experimental rigor, this integrated approach provides more ethical, scalable, and personalized cancer therapeutics.
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.