Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource

Ming Siang Huang, Jen Chieh Han, Pei Yen Lin, Yu Ting You, Richard Tzong Han Tsai, Wen Lian Hsu

研究成果: 雜誌貢獻回顧評介論文同行評審

2 引文 斯高帕斯(Scopus)

摘要

Natural language processing (NLP) has become an essential technique in various fields, offering a wide range of possibilities for analyzing data and developing diverse NLP tasks. In the biomedical domain, understanding the complex relationships between compounds and proteins is critical, especially in the context of signal transduction and biochemical pathways. Among these relationships, protein–protein interactions (PPIs) are of particular interest, given their potential to trigger a variety of biological reactions. To improve the ability to predict PPI events, we propose the protein event detection dataset (PEDD), which comprises 6823 abstracts, 39 488 sentences and 182 937 gene pairs. Our PEDD dataset has been utilized in the AI CUP Biomedical Paper Analysis competition, where systems are challenged to predict 12 different relation types. In this paper, we review the state-of-the-art relation extraction research and provide an overview of the PEDD’s compilation process. Furthermore, we present the results of the PPI extraction competition and evaluate several language models’ performances on the PEDD. This paper’s outcomes will provide a valuable roadmap for future studies on protein event detection in NLP. By addressing this critical challenge, we hope to enable breakthroughs in drug discovery and enhance our understanding of the molecular mechanisms underlying various diseases.

原文???core.languages.en_GB???
文章編號bbae132
期刊Briefings in bioinformatics
25
發行號3
DOIs
出版狀態已出版 - 1 5月 2024

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