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Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0, Open
Journal article
Regulating genome language models: navigating policy challenges at the intersection of AI and genetics
Human genetics
16 Sep 2025
PMID: 40956353
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
Genome Language Models (GLMs) represent a transformative convergence of artificial intelligence (AI) and genomics, offering unprecedented capabilities for biological discovery, healthcare innovation, and therapeutic design applications. However, these powerful tools create novel regulatory challenges that existing frameworks—whether AI governance or genomic privacy protections—cannot adequately address alone. This paper examines the critical regulatory gaps emerging at this intersection, highlighting tensions between AI principles that favor broad data access and genomic governance that demands stringent privacy protections and informed consent. We analyze how GLMs challenge conventional regulatory approaches as they pertain to applications in disease risk prediction, international research collaboration, and open-source model distribution. We propose a multilayered governance framework that combines policy innovations such as regulatory sandboxes and certification frameworks with technical solutions for privacy preservation and model interpretability. By developing adaptive governance strategies that bridge AI and genomic regulation, we can enable responsible GLM innovation while safeguarding individual rights, promoting equity, and addressing emerging biosecurity concerns in this rapidly evolving field.
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Details
- Title
- Regulating genome language models: navigating policy challenges at the intersection of AI and genetics
- Creators
- Bahrad A Sokhansanj - Drexel University, Electrical and Computer EngineeringGail L Rosen (Corresponding Author) - Drexel University, Electrical and Computer Engineering
- Publication Details
- Human genetics
- Publisher
- Springer Nature
- Number of pages
- 22
- Grant note
- United States National Science Foundation: 2107108
This work is supported by the United States National Science Foundation under Grant Number 2107108.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001571970100001
- Scopus ID
- 2-s2.0-105016684746
- Other Identifier
- 991022093055904721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Genetics & Heredity