Over the past decades, computer scientists have introduced numerous artificial intelligence (AI) systems designed to emulate the organization and functioning of networks of neurons in the brain.

The future of AI is here. Discover the world’s first self-evolving, open-weight AI model that can independently upgrade ...

Johns Hopkins and other BRAIN Initiative Cell Atlas Network (BICAN) researchers have enhanced a cellular road map of how the ...

Stanford Medicine researchers have built CRISPR-GPT, a large language model designed to automate the full arc of gene-editing ...

Uncovering complex disease patterns from large-scale, heterogeneous health data remains a significant challenge. Traditional statistical methods and conventional machine learning algorithms often ...

EMBL researchers created SDR-seq, a next-generation tool that decodes both DNA and RNA from the same cell. It finally opens access to non-coding regions, where most disease-associated genetic variants ...

Population geneticists increasingly confront a paradox: even with genome-scale datasets and advanced machine learning models, subtle population structure often remains undetected, particularly in ...

Proteogenomics explores how genetic information translates into protein expression and function, and the role of changes across DNA, RNA, and proteins in influencing disease development and ...

A key question in artificial intelligence is how often models go beyond just regurgitating and remixing what they have learned and produce truly novel ideas or insights. A new project from Google ...

Researchers from Children's Hospital of Philadelphia (CHOP) and the Perelman School of Medicine at the University of Pennsylvania (Penn Medicine) have successfully employed an algorithm to identify ...