arXiv:2606.00685v1 Announce Type: new Abstract: Gene regulatory networks (GRNs) capture transcription factor-target interactions and are central to understanding cell-state regulation and disease. Reconstructing GRNs from paired single-cell transcriptomic and chromatin accessibility data is promising but challenging: scATAC is extremely sparse, and most methods rely on fixed peak-to-gene links and weak supervision. We present EpiAwareNet, a prior-guided multi-omic Transformer framework that reconstructs GRNs from paired single-cell data using only lightweight biological priors. In Stage 1, Epi
Advances in AI, particularly transformer architectures, are increasingly being applied to complex biological data, enabling more sophisticated and less resource-intensive analyses of cellular processes.
Improved gene regulatory network inference is crucial for understanding disease mechanisms, developing targeted therapies, and advancing synthetic biology platforms, impacting medicine and biotechnology.
The ability to reconstruct gene regulatory networks with lightweight biological priors and multi-omic single-cell data will accelerate drug discovery, personalized medicine, and potentially bioengineering.
- · Biotechnology companies
- · Pharmaceutical research
- · Genomic sequencing providers
- · AI algorithm developers
- · Traditional drug discovery methods
- · Less data-driven biological research
- · Diseases with complex genomic origins
More accurate and efficient identification of therapeutic targets for complex diseases.
Acceleration of synthetic biology applications through a deeper understanding of cellular control mechanisms.
The development of highly personalized medical treatments based on individual genomic and cellular profiles.
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Read at arXiv cs.LG