By analyzing a massive and diverse microbiome dataset, they developed a genomic language model (gLM) that learns to understand the functional “semantics” and regulatory “syntax” of each gene. The gLM model, like large language models, learns through introspection; This means it learns meaningful gene representations from data without the need for human labels.
The researchers found that the gLM model can recognize enzyme functions, co-regulated gene modules, and provide genomic context that can predict gene functions. This allows us to more accurately understand the functions of genes and the relationships between them.
The gLM model represents a significant advance in bioinformatics and machine learning that can accelerate the discovery of new biological mechanisms and improve the understanding of genomic information.
Source: Ferra
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