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生物文本探索者v2.0：一种用于生物医学文献挖掘和概念发现的改进工具。

BioTextQuest v2.0: An evolved tool for biomedical literature mining and concept discovery.

作者信息

Theodosiou Theodosios, Vrettos Konstantinos, Baltsavia Ismini, Baltoumas Fotis, Papanikolaou Nikolas, Antonakis Andreas Ν, Mossialos Dimitrios, Ouzounis Christos A, Promponas Vasilis J, Karaglani Makrina, Chatzaki Ekaterini, Brandau Sven, Pavlopoulos Georgios A, Andreakos Evangelos, Iliopoulos Ioannis

机构信息

Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece.

Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Athens 16672, Greece.

出版信息

Comput Struct Biotechnol J. 2024 Aug 21;23:3247-3253. doi: 10.1016/j.csbj.2024.08.016. eCollection 2024 Dec.

DOI:10.1016/j.csbj.2024.08.016
PMID:39279874
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11399685/
Abstract

The process of navigating through the landscape of biomedical literature and performing searches or combining them with bioinformatics analyses can be daunting, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related repositories. Herein, we present BioTextQuest v2.0, a tool for biomedical literature mining. BioTextQuest v2.0 is an open-source online web portal for document clustering based on sets of selected biomedical terms, offering efficient management of information derived from PubMed abstracts. Employing established machine learning algorithms, the tool facilitates document clustering while allowing users to customize the analysis by selecting terms of interest. BioTextQuest v2.0 streamlines the process of uncovering valuable insights from biomedical research articles, serving as an agent that connects the identification of key terms like genes/proteins, diseases, chemicals, Gene Ontology (GO) terms, functions, and others through named entity recognition, and their application in biological research. Instead of manually sifting through articles, researchers can enter their PubMed-like query and receive extracted information in two user-friendly formats, tables and word clouds, simplifying the comprehension of key findings. The latest update of BioTextQuest leverages the EXTRACT named entity recognition tagger, enhancing its ability to pinpoint various biological entities within text. BioTextQuest v2.0 acts as a research assistant, significantly reducing the time and effort required for researchers to identify and present relevant information from the biomedical literature.

摘要

鉴于科学文献库呈指数级增长，以及为挖掘PubMed（®）及相关知识库而设计的大量工具，浏览生物医学文献、进行检索或将其与生物信息学分析相结合的过程可能令人望而生畏。在此，我们展示BioTextQuest v2.0，一种用于生物医学文献挖掘的工具。BioTextQuest v2.0是一个基于选定生物医学术语集进行文档聚类的开源在线门户网站，可对从PubMed摘要中获取的信息进行有效管理。该工具采用成熟的机器学习算法，在方便用户通过选择感兴趣的术语来定制分析的同时，促进文档聚类。BioTextQuest v2.0简化了从生物医学研究文章中发现有价值见解的过程，它充当了一个媒介，通过命名实体识别连接基因/蛋白质、疾病、化学物质、基因本体（GO）术语、功能等关键术语的识别，并将其应用于生物学研究。研究人员无需手动筛选文章，只需输入类似PubMed的查询，就能以两种用户友好的格式（表格和词云）接收提取的信息，从而简化对关键发现的理解。BioTextQuest的最新更新利用了EXTRACT命名实体识别标记器，增强了其在文本中精确识别各种生物实体的能力。BioTextQuest v2.0充当研究助手，显著减少了研究人员从生物医学文献中识别和呈现相关信息所需的时间和精力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2338/11399685/c3d1c63bb468/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2338/11399685/c38337fd5ebe/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2338/11399685/99cbe841a385/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2338/11399685/ba3d324472d3/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2338/11399685/c3d1c63bb468/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2338/11399685/c38337fd5ebe/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2338/11399685/99cbe841a385/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2338/11399685/ba3d324472d3/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2338/11399685/c3d1c63bb468/gr4.jpg

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