A groundbreaking study by Yunha Hwang and team has developed gLM, an AI system that decodes the complex language of genomics from extensive microbial data. This innovation enables a deeper understanding of gene functions and regulations, leading to new discoveries in genomics. gLM exemplifies the potential of AI in advancing life sciences and tackling global challenges. Credit: SciTechDaily.com
The study demonstrates that gLM learns enzymatic functions and co-regulated gene modules , and provides genomic context that can predict gene function. The model also learns taxonomic information and context-dependencies of gene functions. Strikingly, gLM does not know which enzyme it is seeing, nor what bacteria the sequence comes from.
“Traditional functional annotation methods typically focus on one protein at a time, ignoring the interactions across proteins. gLM represents a major advancement by integrating the concept of gene neighborhoods with language models, thereby providing a more comprehensive view of protein interactions,” stated Martin Steinegger , an expert in bioinformatics and machine learning, who was not involved in the study.
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