ARC Innovation at Sheba Medical Center and Icahn School of Medicine at Mount Sinai (part of the Mount Sinai Health System in New York City) today announced a landmark three-year collaboration with NVIDIA to harness the power of artificial intelligence for genomic discovery through the application of large language model (LLM) technology. The initiative aims to pioneer the decoding of the majority of the human genome that remains poorly understood in order to unlock new pathways for disease prevention, diagnosis, and treatment. Over the three-year period, the project will be supported by significant investment from the participating organizations, with the goal of accelerating research that can ultimately benefit patients worldwide.

Deep brain stimulation – implants in the brain that act as a kind of ‘pacemaker’ – has led to clinical improvements in half of the participants with treatment-resistant severe depression in an ‘open label’ trial. Significantly, the study, led by researchers in the UK and China, identified a telltale signature of brain activity that predicted how well individual patients responded to the treatment. This could be used in future to target the treatment at those patients most likely to benefit.

Summary

The antimicrobial resistance (AMR) portal is a central hub for accessing AMR genotype and phenotype data.

Built on EMBL-EBI infrastructure in collaboration with researchers from Imperial College London, the AMR portal connects many EMBL-EBI open data resources and includes newly annotated bacterial genomes.

The portal enables researchers to browse, analyse and download AMR data to support research, as well as the development of machine-learning tools. 

A research team has tested how well three large language models can detect overlaps and redundancies in clinical questionnaires on mental illness / publication in ‘Nature Mental Health’