by Zaara Abbas / April 26, 2024
In an exciting development at the intersection of artificial intelligence and biology, researchers have developed a groundbreaking AI system called gLM that can decipher the intricate language of genomics. This remarkable innovation, led by Yunha Hwang and an interdisciplinary team at Harvard University, has the potential to unlock new discoveries in the field of genomics and advance our understanding of gene functions and regulations.
Genomics, the study of an organism's genetic makeup, has long been a complex and challenging concept. The vast quantity and diversity of genomic data have outpaced our ability to comprehend and interpret it effectively. Genomic Language Model (gLM) is an AI system that can "read" and comprehend the genomic language, much like how language models like ChatGPT understand human language. By feeding gLM an immense amount of microbial metagenomic data from various environments, the system has learned to understand the functional "semantics" and regulatory "syntax" of genes. It can identify relationships between genes and their genomic contexts, revealing the hidden meanings and interactions that were previously overlooked.
One of the remarkable capabilities of gLM is its ability to provide insights into poorly annotated genomes. Even for well-studied organisms like humans and E. coli, a significant portion of their genes remain poorly characterized. gLM can provide information on these unknown genes by using the contextual information and evolutionary relationships it has learned from the vast genomic data. Moreover, gLM can uncover biological patterns and mechanisms that traditional annotation methods might overlook. By considering the interactions and relationships between genes, rather than analyzing them in isolation, gLM provides a more comprehensive and nuanced understanding of genomic functions. The implications of this innovation are far-reaching.
Researchers envision gLM as a powerful tool to speed up the discovery of biotechnological solutions for global challenges such as climate change and the development of a sustainable bioeconomy. While the potential of gLM is immense, it is important to note that this AI system is not a substitute for experimental validation and the expertise of biologists. Instead, it serves as a complementary tool that provides guidance and insights to inform and quicken the research process. As we continue to explore the use of AI and its applications in various fields, innovations like gLM demonstrate the power of interdisciplinary collaboration.
Citations:
Harvard, U. (2024, April 23). New Harvard-developed AI system unlocks Biology’s source code. SciTechDaily. https://scitechdaily.com/new-harvard-developed-ai-system-unlocks-biologys-source-code/
Hwang, Y., Cornman, A. L., Kellogg, E. H., Ovchinnikov, S., & Girguis, P. R. (2024, April 3). Genomic language model predicts protein co-regulation and function. Nature Communications. https://www.nature.com/articles/s41467-024-46947-9
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