基于 BioBERT 的深度学习和合并 ChemProt-DrugProt 用于增强生物医学关系提取

Bridget T. McInnes, Jiawei Tang, Darshini Mahendran, Mai H. Nguyen
{"title":"基于 BioBERT 的深度学习和合并 ChemProt-DrugProt 用于增强生物医学关系提取","authors":"Bridget T. McInnes, Jiawei Tang, Darshini Mahendran, Mai H. Nguyen","doi":"arxiv-2405.18605","DOIUrl":null,"url":null,"abstract":"This paper presents a methodology for enhancing relation extraction from\nbiomedical texts, focusing specifically on chemical-gene interactions.\nLeveraging the BioBERT model and a multi-layer fully connected network\narchitecture, our approach integrates the ChemProt and DrugProt datasets using\na novel merging strategy. Through extensive experimentation, we demonstrate\nsignificant performance improvements, particularly in CPR groups shared between\nthe datasets. The findings underscore the importance of dataset merging in\naugmenting sample counts and improving model accuracy. Moreover, the study\nhighlights the potential of automated information extraction in biomedical\nresearch and clinical practice.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"32 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction\",\"authors\":\"Bridget T. McInnes, Jiawei Tang, Darshini Mahendran, Mai H. Nguyen\",\"doi\":\"arxiv-2405.18605\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a methodology for enhancing relation extraction from\\nbiomedical texts, focusing specifically on chemical-gene interactions.\\nLeveraging the BioBERT model and a multi-layer fully connected network\\narchitecture, our approach integrates the ChemProt and DrugProt datasets using\\na novel merging strategy. Through extensive experimentation, we demonstrate\\nsignificant performance improvements, particularly in CPR groups shared between\\nthe datasets. The findings underscore the importance of dataset merging in\\naugmenting sample counts and improving model accuracy. Moreover, the study\\nhighlights the potential of automated information extraction in biomedical\\nresearch and clinical practice.\",\"PeriodicalId\":501325,\"journal\":{\"name\":\"arXiv - QuanBio - Molecular Networks\",\"volume\":\"32 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Molecular Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2405.18605\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Molecular Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2405.18605","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

我们的方法利用 BioBERT 模型和多层全连接网络架构,采用新颖的合并策略整合了 ChemProt 和 DrugProt 数据集。通过广泛的实验,我们证明了性能的显著提高,尤其是在数据集之间共享的 CPR 组中。这些发现强调了数据集合并在增加样本数量和提高模型准确性方面的重要性。此外,这项研究还凸显了自动信息提取在生物医学研究和临床实践中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction
This paper presents a methodology for enhancing relation extraction from biomedical texts, focusing specifically on chemical-gene interactions. Leveraging the BioBERT model and a multi-layer fully connected network architecture, our approach integrates the ChemProt and DrugProt datasets using a novel merging strategy. Through extensive experimentation, we demonstrate significant performance improvements, particularly in CPR groups shared between the datasets. The findings underscore the importance of dataset merging in augmenting sample counts and improving model accuracy. Moreover, the study highlights the potential of automated information extraction in biomedical research and clinical practice.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Multi-variable control to mitigate loads in CRISPRa networks Some bounds on positive equilibria in mass action networks Non-explosivity of endotactic stochastic reaction systems Limits on the computational expressivity of non-equilibrium biophysical processes When lowering temperature, the in vivo circadian clock in cyanobacteria follows and surpasses the in vitro protein clock trough the Hopf bifurcation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1