Changing word meanings in biomedical literature reveal pandemics and new technologies.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2023-05-05 DOI:10.1186/s13040-023-00332-2
David N Nicholson, Faisal Alquaddoomi, Vincent Rubinetti, Casey S Greene
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Abstract

While we often think of words as having a fixed meaning that we use to describe a changing world, words are also dynamic and changing. Scientific research can also be remarkably fast-moving, with new concepts or approaches rapidly gaining mind share. We examined scientific writing, both preprint and pre-publication peer-reviewed text, to identify terms that have changed and examine their use. One particular challenge that we faced was that the shift from closed to open access publishing meant that the size of available corpora changed by over an order of magnitude in the last two decades. We developed an approach to evaluate semantic shift by accounting for both intra- and inter-year variability using multiple integrated models. This analysis revealed thousands of change points in both corpora, including for terms such as 'cas9', 'pandemic', and 'sars'. We found that the consistent change-points between pre-publication peer-reviewed and preprinted text are largely related to the COVID-19 pandemic. We also created a web app for exploration that allows users to investigate individual terms ( https://greenelab.github.io/word-lapse/ ). To our knowledge, our research is the first to examine semantic shift in biomedical preprints and pre-publication peer-reviewed text, and provides a foundation for future work to understand how terms acquire new meanings and how peer review affects this process.

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生物医学文献中不断变化的词义揭示了流行病和新技术。
我们通常认为,词语具有固定的含义,用来描述不断变化的世界,但词语也是动态变化的。科学研究也是瞬息万变的,新概念或新方法会迅速占据人们的心智。我们研究了科学著作,包括预印本和出版前的同行评议文本,以确定已发生变化的术语并研究其使用情况。我们面临的一个特殊挑战是,从封闭式出版到开放式出版的转变意味着可用语料库的规模在过去二十年中发生了超过一个数量级的变化。我们开发了一种评估语义变化的方法,利用多种综合模型对年内和年际的变化进行考量。这项分析揭示了两个语料库中的数千个变化点,包括 "cas9"、"pandemic "和 "sars "等术语。我们发现,出版前同行评审文本和预印文本之间的一致变化点在很大程度上与 COVID-19 大流行有关。我们还创建了一个用于探索的网络应用程序,允许用户调查单个术语 ( https://greenelab.github.io/word-lapse/ )。据我们所知,我们的研究是首次研究生物医学预印本和出版前同行评审文本中的语义变化,为今后了解术语如何获得新含义以及同行评审如何影响这一过程奠定了基础。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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