arXiv:2605.28659v1 Announce Type: new Abstract: Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene-expression matrices. However, these approaches predominantly operate in static settings and do not explicitly model the temporal evolution of developmental programs in the cell. Modeling such dynamics is important for understanding how cellular states progressively emerge, differentiate, and reorganize during development or disease progression. In this work-in-progress paper, we investigate an al
The proliferation of advanced AI techniques, particularly transformer architectures, is enabling novel applications in complex biological modeling, moving beyond static analyses to dynamic system predictions.
This development indicates a significant leap in understanding cellular processes, which is foundational for drug discovery, disease modeling, and the broader field of synthetic biology.
The ability to model temporal evolution in biological systems with AI shifts our understanding from snapshots to dynamic narratives, potentially accelerating the development cycle for biological interventions.
- · Biotech companies (drug discovery)
- · AI/ML researchers
- · Synthetic biology startups
- · Healthcare providers
- · Traditional drug discovery methods
- · Biology labs reliant on static models
Improved prediction of disease progression and therapeutic responses at a cellular level.
Acceleration of personalized medicine and the development of highly targeted biological interventions.
Enhanced ability to engineer biological systems for purposes beyond human health, extending to materials science or environmental remediation.
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Read at arXiv cs.LG