ReviewHuman genomics2026
Translating multi-omics into healthcare: requisites for scalable and equitable implementation.
Review in Human genomics, 2026. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence-Driven Reproductive Bioengineering: Integrating Fertility Diagnostics, Organ-on-Chip Systems, Cryobiology and Epigenetic Safety for Precision Reproductive Medicine.Bioengineering (Basel, Switzerland) · 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
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-omics in combination with advanced computational methodologies synthesizes diverse omics data to provide deeper insights into molecular interactions and offers transformative potential for unravelling phenomenon behind disease complexities, improving diagnostics, disease prevention, and personalized treatments. This integrative strategy enables our understanding of gene-environment relationships, chronic disease progression, and the intricate molecular pathways involved in health. Effective multi-omics analyses require robust data sharing, accessibility, interoperability, and governance, which are critical for linking genomic elements to phenotypic traits. The Global Alliance for Genomics and Health advocates for responsible data-sharing practices, by promoting key principles such as transparency and equity. By emphasizing a collaborative approach to data utilization, our proposed framework seeks to advance improved disease prevention and treatment strategies. Multi-disciplinary collaboration, encompassing researchers, clinicians, policy makers, and patient representatives, is pivotal for driving innovation and addressing rare disease diagnostics. The success of multi-omics applications hinges on the establishment of comprehensive datasets, understanding the functional implications of multi-omic variation, adherence to findable, accessible, interoperable, reusable (FAIR) and Collective Benefit, Authority to Control, Responsibility, and Ethics (CARE) principles, and the strengthening of global genomic commons, benefiting scientific research, drug development, and broader health initiatives. Our review highlights essential components of multi-omics integration, underscoring its potential to transform the landscape of precision medicine and improved patient outcomes worldwide.
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.