ReviewBriefings in bioinformatics2026
Toward trustworthy artificial intelligence in multi-omics: a review of reproducibility, stability, and interpretability.
Review in Briefings in bioinformatics, 2026. 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.
- Multi-Omics-Guided Design and Safety Engineering of Nucleic Acid Therapeutics: From Molecular Perturbation to Predictive Toxicology and Precision Translation.Chemical biology & drug design · 2026Review
- Exposome influences: a multi-omics perspective on the combined toxic effects of pharmaceuticals and personal care products in Alzheimer's disease.Frontiers in toxicology · 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
2 authors.
Funding
Abstract
The integration of multi-omics data has become increasingly important in advancing precision medicine and systems biology. However, the reliability and trustworthiness of artificial intelligence (AI) models applied to such data remain critical concerns. This review examines the evolution and current landscape of reproducibility, stability, and interpretability in AI-driven multi-omics analysis. We explore these three pillars of trustworthiness in recent literature, with a particular focus on methodological innovations, benchmarking practices, and biological relevance. Drawing from key publications, including those featured in Briefings in Bioinformatics, we highlight emerging frameworks that aim to make multi-omics models more robust, transparent, and translationally meaningful. We advocate for routine adoption of TRUST-aligned evaluation practices, including structured stability assessments, multi-cohort benchmarking, and standardized model-card reporting, as default components of future multi-omics AI development. We conclude by outlining key challenges and future directions for developing trustworthy AI systems capable of supporting reproducible, interpretable, and clinically meaningful multi-omics research.
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.