@inproceedings{park-etal-2023-automated,
    title = "Automated Extraction of Molecular Interactions and Pathway Knowledge using Large Language Model, Galactica: Opportunities and Challenges",
    author = "Park, Gilchan  and
      Yoon, Byung-Jun  and
      Luo, Xihaier  and
      Lpez-Marrero, Vanessa  and
      Johnstone, Patrick  and
      Yoo, Shinjae  and
      Alexander, Francis",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.22",
    doi = "10.18653/v1/2023.bionlp-1.22",
    pages = "255--264",
    abstract = "Understanding protein interactions and pathway knowledge is essential for comprehending living systems and investigating the mechanisms underlying various biological functions and complex diseases. While numerous databases curate such biological data obtained from literature and other sources, they are not comprehensive and require considerable effort to maintain. One mitigation strategies can be utilizing large language models to automatically extract biological information and explore their potential in life science research. This study presents an initial investigation of the efficacy of utilizing a large language model, Galactica in life science research by assessing its performance on tasks involving protein interactions, pathways, and gene regulatory relation recognition. The paper details the results obtained from the model evaluation, highlights the findings, and discusses the opportunities and challenges.",
}

Understanding protein interactions and pathway knowledge is essential for comprehending living systems and investigating the mechanisms underlying various biological functions and complex diseases. While numerous databases curate such biological data obtained from literature and other sources, they are not comprehensive and require considerable effort to maintain. One mitigation strategies can be utilizing large language models to automatically extract biological information and explore their potential in life science research. This study presents an initial investigation of the efficacy of utilizing a large language model, Galactica in life science research by assessing its performance on tasks involving protein interactions, pathways, and gene regulatory relation recognition. The paper details the results obtained from the model evaluation, highlights the findings, and discusses the opportunities and challenges.
Park, Gilchan Yoon, Byung-Jun Luo, Xihaier Lpez-Marrero, Vanessa Johnstone, Patrick Yoo, Shinjae Alex er, Francis
Automated Extraction of Molecular Interactions and Pathway Knowledge using Large Language Model, Galactica: Opportunities and Challenges