利用文字探勘建立醫學主題詞與基因名稱之關聯性

林宜歆; 林嶔; 葉釋仁; 蘇遂龍

doi:10.6288/TJPH.201802_37(1).106086

山西大学，您好！

感谢采购，人社期刊库｜人社论文库，期间：2025/01/01~2025/12/31

感谢试用，科学期刊库｜科学论文库，期间：2024/10/20~2025/01/20

浏览高级检索

关闭
检索检索高级检索出版物检索
浏览期刊主题浏览核心期刊浏览期刊拼音浏览

关闭

查询符号(半角)	查询符号意义说明
空格	表示为「AND」两个查询词之交集
双引号 ( " " )	片语以双引号标示开始及结束，而且只寻找出现顺序相同的字词，例： " image process "
?	表示一个字母切截，输入两个?表两个字母，依此类推，例：输入「Appl?」，查得结果应为appl e , appl y … ( 常用于英文字查询 )
*	表示不限字母切截，由0~n. 例：输入「appl*」，查得结果应为appl e , appl es , appl y , appl ied , appl ication … ( 常用于英文字查询 )
AND、OR、NOT	布尔逻辑组合关键字，用来扩大或缩小查询范围的技巧。 (1) AND ：缩小查询范围 (2) OR ：扩大查询范围(3) NOT：排除不相关的范围

DOI 是数位物件识别码（ D igital O bject I dentifier ）的简称，
为物件在网路上的唯一识别码，可用于永久连结并引用目标物件。

使用DOI作为永久连结

每个DOI号前面加上「 http://dx.doi.org/ 」便成为永久网址。
如以DOI号为 10.5297/ser.1201.002 的文献为例，此文献的永久连结便是： http://dx.doi.org/ 10.5297/ser.1201.002 。
日后不论出版单位如何更动此文献位置，永久连结所指向的位置皆会即时更新，不再错失重要的研究。

引用含有DOI的文献

有DOI的文献在引用时皆应同时引用DOI。若使用APA、Chicago以外未规范DOI的引用格式，可引用DOI永久连结。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。

ACI:

数据来源：Academic Citation Index，简称ACI

台湾最大的引用文献资料库，目前收录台湾与港澳地区所出版的人文学、社会学领域学术期刊之书目资料与参考文献，总期刊量超过690种，每年定期公布收录期刊的影响指数(Impact Factor)等指标给大众，并提供专家学者与学术单位实用的计量与分析功能。

五年影响指数（5-Year Impact Factor）：某一期刊前五年所出版的文章在当年度的平均被引用次数。

公式：（前五年发表论文在统计年的被引用次数）÷（前五年论文产出论文总篇数）

例如：A期刊2017年之五年影响指数

(A期刊2012-2016年发表论文在2017年的被引总次数)/(A期刊2012-2016年发表的论文总数)

什么是预刊文章？

为提供读者最前线之学术资讯，于期刊文献获同意刊登后、纸本印制完成前，率先于网路线上发表之文章即为预刊文章。预刊文章尚未有卷期、页次及出版日期资讯，但可藉由DOI号识别。 DOI号是文献的数位身份证字号，不论预刊或正式出版皆不会改变，读者可点击DOI连结，或于DOI号前面加上「 http://dx.doi.org/ 」连结到文献目前最新版本。

如何引用预刊文章？

请使用预刊文章的线上发表日期及DOI号来引用该篇文献。

引用范例(视不同引文格式规范可能有所差异)：

作者姓名。文章篇名。期刊名称。 YYYY/MM/DD线上预先发表。

doi:DOI 号

来源数据

P12 - 23

医药卫生 > 预防保健与卫生学
医药卫生 > 社会医学

DOI： 10.6288/TJPH

书目管理工具

相关连结

问题回报

全文下载

利用文字探勘建立医学主题词与基因名称之关联性

Associations between medical subject headings and gene names based on text-mining in PubMed

林宜歆(Yi-Hsin Lin) ；林嶔(Chin Lin) ；叶释仁(Shih-Jen Yeh) ；苏遂龙(Sui-Lung Su)

台湾公共卫生杂志； 37 卷 1 期 (2018 / 02 / 15) ， P12 - 23

繁体中文 DOI： 10.6288/TJPH.201802_37(1).106086

文字探勘；医学主题词；基因名称；常一起出现在摘要中的相关性； text-mining ； medical subject headings (MeSH) ； gene names ； often appearing in abstracts

0 个人觉得这篇文章赞

摘要 │ 参考文献 (32) │ 被引用文献 (1) │ 国际计量

摘要〈TOP〉

并列摘要〈TOP〉

Objectives: In recent years, the biomedical literature has expanded by leaps and bounds. Based on studies available in the databases, it is difficult for users to sort through the massive literature and organize sets of qualitative data. At present, there are well-known websites such as Coremine, STRING, and DisGeNet; however, one can inquire only about words directly related to the search words without any indirectly relevant suggestions. Thus, there is a real need to address the issue. In this study, we investigated the number of uses in each year and the relationship between medical subject headings (MeSH) and gene names in the non-structured abstracts in PubMed. Methods: The study used a text-mining design. The study samples were the 26,295,751 articles in PubMed on July 8, 2016. Using the MeSH from the American National Library of Medicine in the MeSHBrowser, we identified 27,883 words to establish the MeSH dictionary. Genes were officially named by the Human Genome Organization Nomenclature Committee. A search of NCBI Gene yielded a dictionary of 39,903 human genes. The medical subject headings and gene names included in the abstracts were then extracted and calculated by year. We used word2vec to analyze the associations between the MeSH and gene names. Results: We built an interactive website which provides information about the number of uses in different years and the relevant words that most often appeared together with MeSH and gene names in the abstract (https://yihsin.shinyapps.io/meshgeneterm_relation/. For example, words which appeared most often together with Osteoarthritis were Osteotomy, Nails, Patella, Knee joint, Physical examination and Paralysis. There are also indirectly relevant suggestions. Conclusions: The website developed in this study provides the number of uses in different years of MeSH and gene names, and what words were most associated with them; this indicates that these words were often mentioned and discussed together in medical publications. We can also see the indirect correlations between them so that researchers exploring new areas can quickly have a general understanding of the field.

参考文献 ( 32 ) 〈TOP〉

U.S. National Library of Medicine. Medical subject headings. Available at: https://www.nlm.nih.gov/mesh/introduction.html. Accessed June, 2016.
PubGene. Coremine medical. Available at: https://www.coremine.com/medical/#search. Accessed November, 2016.
Cook K. Unstructured data and the 80 percent rule. Available at: http://clarabridge.com/default.aspx?tabid=137&ModuleID=635&ArticleID=551. Accessed December 3, 2012
HUGO Gene Nomenclature Committee. Website information. Available at: http://www.genenames.org/. Accessed June, 2016.
Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. Available at: https://arxiv.org/abs/1310.4546. Accessed June, 2016.

被引用文献 ( 1 ) 〈TOP〉

张益诚,张育杰,余泰毅(2021)。探讨环境教育论文的文件自动分类技术－以2013－2018年环境教育研讨会摘要为例。环境教育研究，17(1)，85-128。

国际计量

〈TOP〉

E-mail ：

文章公开取用时，将寄通知信至您填写的信箱地址