@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Automated Extraction of Molecular Interactions and Pathway Knowledge using Large Language Model, Galactica: Opportunities and Challenges
%A Park, Gilchan
%A Yoon, Byung-Jun
%A Luo, Xihaier
%A Lpez-Marrero, Vanessa
%A Johnstone, Patrick
%A Yoo, Shinjae
%A Alexander, Francis
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F park-etal-2023-automated
%X 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.
%R 10.18653/v1/2023.bionlp-1.22
%U https://aclanthology.org/2023.bionlp-1.22
%U https://doi.org/10.18653/v1/2023.bionlp-1.22
%P 255-264
Markdown (Informal)
[Automated Extraction of Molecular Interactions and Pathway Knowledge using Large Language Model, Galactica: Opportunities and Challenges](https://aclanthology.org/2023.bionlp-1.22) (Park et al., BioNLP 2023)
ACL
- Gilchan Park, Byung-Jun Yoon, Xihaier Luo, Vanessa Lpez-Marrero, Patrick Johnstone, Shinjae Yoo, and Francis Alexander. 2023. Automated Extraction of Molecular Interactions and Pathway Knowledge using Large Language Model, Galactica: Opportunities and Challenges. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 255–264, Toronto, Canada. Association for Computational Linguistics.