Evidence map›Paper›PMID 41728195›Full record

ArticleFrontiers in plant science2025

An AI-driven framework for enhancing regulatory precision and efficiency in CRISPR-Cas gene-edited crops: challenges, opportunities, and global harmonization.

Feng Zhu, Zihan Liu, Zeyu Zheng

Abstract read
In one paragraph

Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Feng ZhuSchool of Medicine, Pingdingshan University, Pingdingshan, Henan, China.
Zihan LiuSchool of Medicine, Pingdingshan University, Pingdingshan, Henan, China.
Zeyu ZhengSchool of Information Engineering, Fuzhou University, Fuzhou, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The rapid advancement and adoption of CRISPR-Cas technologies in crop improvement has significantly outpaced existing regulatory frameworks, leading to inconsistencies in the global oversight of gene-edited organisms. As governments and international bodies struggle to reconcile scientific innovation with policy governance, a pressing need has emerged for methodologies that can translate biological edits into regulatory-compliant representations across jurisdictions. Traditional approaches often compartmentalize genomic and legal domains, lacking the formalism to bridge biological intent and compliance precision. These methods are typically static, unable to adapt to jurisdictional policy drift or incorporate real-time exemption logic, thereby undermining both regulatory interpretability and technical fidelity. Methods: To address this gap, I propose a unified computational framework built around the novel GeneRegAlignNet model and the Constraint-Aware Policy Induction (CAPI) strategy. This framework embeds regulatory semantics directly into the learning architecture, enabling the alignment of gene-editing features with heterogeneous policy descriptors in a shared latent space. GeneRegAlignNet employs symbolic gating, contrastive manifold learning, and exemption-aware vectorization to predict alignment likelihoods between edits and legal categories with high precision. CAPI extends this model with a risk-calibrated policy optimization pipeline that accounts for policy evolution, regulatory variance, and jurisdictional priorities. Results and Discussion: Empirical validation demonstrates improved performance in regulatory alignment accuracy and resilience to policy drift across a diverse set of gene-editing scenarios. By tightly integrating formal representations of molecular edits with dynamic, multi-jurisdictional policy inference, our framework offers a scalable and interpretable path forward in enhancing regulatory precision and global harmonization in the oversight of CRISPR-Cas-edited crops.

Indexed as

biosafety complianceConstraint-Aware Policy Induction (CAPI) strategyCRISPR-Cagene-edited cropsGeneRegAlignNet mode

Identifiers

PMID41728195
PMCPMC12916714

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.