New AI model improves prediction power for genomics related to disease

To understand the workings of DNA in relation to disease, scientists at Los Alamos National Laboratory have developed the first multimodal deep learning model of its kind, EPBDxDNABERT-2, capable of ascertaining the precise relationship between transcription factors, proteins that regulate gene activities, leveraging an aspect of DNA called DNA breathing, in which the double-helix structure opens and closes spontaneously. The model has the potential to aid in the design of drugs used to treat diseases that originate in gene activity. To understand the workings of DNA in relation to disease, scientists at Los Alamos National Laboratory have developed the first multimodal deep learning model of its kind, EPBDxDNABERT-2, capable of ascertaining the precise relationship between transcription factors, proteins that regulate gene activities, leveraging an aspect of DNA called DNA breathing, in which the double-helix structure opens and closes spontaneously. The model has the potential to aid in the design of drugs used to treat diseases that originate in gene activity. Biotechnology Molecular & Computational biology Phys.org – latest science and technology news stories