arXiv:2602.02014v2 Announce Type: replace-cross Abstract: Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However, exhaustive sequential reading is structurally misaligned with sparse and discontinuous genomic semantics, leading to wasted computation on low-information background and preventing understanding-driven compression for long contexts. Here, we present OpticalDNA, a vision-based framework that reframes genomic modeling as Optical Character Recognition (OCR)-style document understanding. OpticalDNA r
The proliferation of genomic data and the limitations of current large language model architectures for genomic analysis are driving innovation in more efficient and semantically aligned modeling approaches.
This new vision-based framework could significantly improve the efficiency and accuracy of genomic modeling, accelerating drug discovery, synthetic biology, and personalized medicine.
Genomic modeling might shift from sequential token processing to a more efficient, vision-based approach, unlocking better understanding of complex biological information.
- · Biotech companies
- · Pharmaceutical R&D
- · AI researchers (vision/genomics)
- · Personalized medicine
- · Companies reliant solely on traditional genomic NLP
- · Inefficient genomic sequencing methods
More accurate and faster identification of genetic markers for diseases and traits becomes possible.
This could lead to a wave of new therapeutic targets and advanced gene-editing applications.
The ability to 'read' DNA more effectively could accelerate the design and synthesis of novel biological systems and materials.
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Read at arXiv cs.LG