• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于词表示和凝聚聚类算法创建神经科学知识组织系统。

Creating Neuroscientific Knowledge Organization System Based on Word Representation and Agglomerative Clustering Algorithm.

作者信息

Huangfu Cunqing, Zeng Yi, Wang Yuwei

机构信息

Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

University of Chinese Academy of Sciences, Beijing, China.

出版信息

Front Neuroinform. 2020 Aug 18;14:38. doi: 10.3389/fninf.2020.00038. eCollection 2020.

DOI:10.3389/fninf.2020.00038
PMID:33013345
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7461893/
Abstract

The literature on neuroscience has grown rapidly in recent years with the emergence of new domains of research. In the context of this progress, creating a knowledge organization system (KOS) that can quickly incorporate terms of a given domain is an important aim in the area. In this article, we develop a systematic method based on word representation and the agglomerative clustering algorithm to semi-automatically build a hierarchical KOS. We collected 35,832 research keywords and 11,497 research methods from PubMed Central database, and organized them in a hierarchical structure according to semantic distance. We show that the proposed KOS can help find terms related to the given topics, analyze articles related to specific domains of research, and characterize the features of article clusters. The proposed method can significantly reduce the manual work required by experts to organize the KOS.

摘要

近年来，随着神经科学新研究领域的出现，该领域的文献增长迅速。在这一进展的背景下，创建一个能够快速纳入特定领域术语的知识组织系统（KOS）是该领域的一个重要目标。在本文中，我们开发了一种基于词表示和凝聚聚类算法的系统方法，以半自动构建分层KOS。我们从PubMed Central数据库中收集了35,832个研究关键词和11,497种研究方法，并根据语义距离将它们组织成层次结构。我们表明，所提出的KOS有助于找到与给定主题相关的术语，分析与特定研究领域相关的文章，并表征文章聚类的特征。所提出的方法可以显著减少专家组织KOS所需的人工工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/6dd0708efe2c/fninf-14-00038-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/e3ef2e3750d1/fninf-14-00038-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/e0886354844c/fninf-14-00038-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/ee5a48e7fe92/fninf-14-00038-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/3d6cd5c25869/fninf-14-00038-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/25bad9446ffc/fninf-14-00038-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/45af5ee1578b/fninf-14-00038-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/43cfe93a51d8/fninf-14-00038-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/8d13fe12e935/fninf-14-00038-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/6dd0708efe2c/fninf-14-00038-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/e3ef2e3750d1/fninf-14-00038-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/e0886354844c/fninf-14-00038-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/ee5a48e7fe92/fninf-14-00038-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/3d6cd5c25869/fninf-14-00038-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/25bad9446ffc/fninf-14-00038-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/45af5ee1578b/fninf-14-00038-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/43cfe93a51d8/fninf-14-00038-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/8d13fe12e935/fninf-14-00038-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b8/7461893/6dd0708efe2c/fninf-14-00038-g009.jpg

相似文献

1
Creating Neuroscientific Knowledge Organization System Based on Word Representation and Agglomerative Clustering Algorithm.基于词表示和凝聚聚类算法创建神经科学知识组织系统。
Front Neuroinform. 2020 Aug 18;14:38. doi: 10.3389/fninf.2020.00038. eCollection 2020.
2
Reengineering of MeSH thesauri for term selection to optimize literature retrieval and knowledge reconstruction in support of stem cell research.重新设计医学主题词表以进行术语选择，以优化文献检索和知识重建，支持干细胞研究。
BMC Med Inform Decis Mak. 2016 May 23;16:54. doi: 10.1186/s12911-016-0298-z.
3
Method for Determining the Optimal Number of Clusters Based on Agglomerative Hierarchical Clustering.基于凝聚层次聚类的最佳聚类数确定方法。
IEEE Trans Neural Netw Learn Syst. 2017 Dec;28(12):3007-3017. doi: 10.1109/TNNLS.2016.2608001. Epub 2016 Oct 5.
4
Automatic Quasi-Clique Merger Algorithm - a Hierarchical Clustering Based on Subgraph-Density.自动准团合并算法——一种基于子图密度的层次聚类算法
Physica A. 2022 Jan 1;585. doi: 10.1016/j.physa.2021.126442. Epub 2021 Sep 24.
5
DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems.DyKOSMap：生物医学知识组织系统间映射适应性的框架
J Biomed Inform. 2015 Jun;55:153-73. doi: 10.1016/j.jbi.2015.04.001. Epub 2015 Apr 15.
6
[The hierarchical clustering analysis of hyperspectral image based on probabilistic latent semantic analysis].基于概率潜在语义分析的高光谱图像层次聚类分析
Guang Pu Xue Yu Guang Pu Fen Xi. 2011 Sep;31(9):2471-5.
7
Discovering themes in biomedical literature using a projection-based algorithm.基于投影算法的生物医学文献主题发现
BMC Bioinformatics. 2018 Jul 16;19(1):269. doi: 10.1186/s12859-018-2240-0.
8
LEGClust- a clustering algorithm based on layered entropic subgraphs.LEGClust——一种基于分层熵子图的聚类算法。
IEEE Trans Pattern Anal Mach Intell. 2008 Jan;30(1):62-75. doi: 10.1109/TPAMI.2007.1142.
9
An On-line agglomerative clustering method for nonstationary data.一种用于非平稳数据的在线凝聚聚类方法。
Neural Comput. 1999 Feb 15;11(2):521-40. doi: 10.1162/089976699300016755.
10
A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development.利用生物医学文献中的语义谓词构建的联合药物治疗知识图谱：算法开发
JMIR Med Inform. 2020 Apr 28;8(4):e18323. doi: 10.2196/18323.

引用本文的文献

1
AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction.AT-神经自身免疫性脑脊髓炎：一种用于面向研究共享的神经影像来源构建的带属性事件联合提取模型。
Front Neurosci. 2022 Mar 7;15:739535. doi: 10.3389/fnins.2021.739535. eCollection 2021.

本文引用的文献

1
Research Trend Visualization by MeSH Terms from PubMed.基于 PubMed 的 MeSH 术语的研究趋势可视化。
Int J Environ Res Public Health. 2018 May 30;15(6):1113. doi: 10.3390/ijerph15061113.
2
Analysis of MeSH Indexing Patterns and Frequency of Predicates.医学主题词表（MeSH）标引模式及谓词频率分析
Stud Health Technol Inform. 2018;247:666-670.
3
Redefining the pharmacology and pharmacy subject category in the journal citation reports using medical subject headings (MeSH).使用医学主题词表（MeSH）重新定义《期刊引证报告》中的药理学与药学学科类别。
Int J Clin Pharm. 2017 Oct;39(5):989-997. doi: 10.1007/s11096-017-0527-2. Epub 2017 Aug 23.
4
The Changing Landscape of Neuroscience Research, 2006-2015: A Bibliometric Study.2006 - 2015年神经科学研究的变化态势：一项文献计量学研究
Front Neurosci. 2017 Mar 21;11:120. doi: 10.3389/fnins.2017.00120. eCollection 2017.
5
MeSHDD: Literature-based drug-drug similarity for drug repositioning.医学主题词表驱动的药物-药物相似性用于药物重新定位
J Am Med Inform Assoc. 2017 May 1;24(3):614-618. doi: 10.1093/jamia/ocw142.
6
ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data.ETE 3：系统发育基因组数据的重建、分析与可视化
Mol Biol Evol. 2016 Jun;33(6):1635-8. doi: 10.1093/molbev/msw046. Epub 2016 Feb 26.
7
Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila.蘑菇体输出神经元编码效价并指导果蝇基于记忆的行动选择。
Elife. 2014 Dec 23;3:e04580. doi: 10.7554/eLife.04580.
8
The neuronal architecture of the mushroom body provides a logic for associative learning.蘑菇体的神经元结构为联想学习提供了一种逻辑。
Elife. 2014 Dec 23;3:e04577. doi: 10.7554/eLife.04577.
9
Disease Ontology: a backbone for disease semantic integration.疾病本体论：疾病语义集成的骨干。
Nucleic Acids Res. 2012 Jan;40(Database issue):D940-6. doi: 10.1093/nar/gkr972. Epub 2011 Nov 12.
10
Clustering by passing messages between data points.通过在数据点之间传递信息进行聚类。
Science. 2007 Feb 16;315(5814):972-6. doi: 10.1126/science.1136800. Epub 2007 Jan 11.