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Phys.org — Biology · September 9, 2026

New AI model for DNA learns from evolution to unlock secrets of the human genome

BiologyData ScienceMedicine
THE AI ANGLE
Predicting pathogenic and functional genetic variants across genomes

Researchers at UC Berkeley developed GPN-Star, a genomic language model trained on whole-genome alignments that accurately identifies functional elements and predicts the pathogenicity of genetic variants. By leveraging evolutionary conservation across different timescales, the model outpaces competitors while drastically reducing training requirements from thousands of processors over months to a handful of processors over hours or days. This approach provides an accessible computational framework to prioritize experimentally testing noncoding and coding mutations linked to complex diseases like cancer and schizophrenia.

THE TEACHING ANGLE
Instructors can explore the pedagogical tension between brute-force scaling on raw, unaligned sequence data versus engineering models with biological priors, showing how evolutionary timescales dictate model efficacy for protein mutations versus complex noncoding traits.

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