Mining impactful discoveries from the biomedical literature

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Erwan Moreau, Orla Hardiman, Mark Heverin, Declan O’Sullivan
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引用次数: 0

Abstract

Literature-based discovery (LBD) aims to help researchers to identify relations between concepts which are worthy of further investigation by text-mining the biomedical literature. While the LBD literature is rich and the field is considered mature, standard practice in the evaluation of LBD methods is methodologically poor and has not progressed on par with the domain. The lack of properly designed and decent-sized benchmark dataset hinders the progress of the field and its development into applications usable by biomedical experts. This work presents a method for mining past discoveries from the biomedical literature. It leverages the impact made by a discovery, using descriptive statistics to detect surges in the prevalence of a relation across time. The validity of the method is tested against a baseline representing the state-of-the-art “time-sliced” method. This method allows the collection of a large amount of time-stamped discoveries. These can be used for LBD evaluation, alleviating the long-standing issue of inadequate evaluation. It might also pave the way for more fine-grained LBD methods, which could exploit the diversity of these past discoveries to train supervised models. Finally the dataset (or some future version of it inspired by our method) could be used as a methodological tool for systematic reviews. We provide an online exploration tool in this perspective, available at https://brainmend.adaptcentre.ie/ .
从生物医学文献中挖掘有影响力的发现
基于文献的发现(LBD)旨在帮助研究人员通过对生物医学文献进行文本挖掘,找出值得进一步研究的概念之间的关系。虽然基于文献的发现(LBD)文献十分丰富,该领域也被认为是成熟的,但对基于文献的发现(LBD)方法进行评估的标准实践在方法论上并不完善,与该领域的进展不相称。缺乏设计合理、规模适当的基准数据集阻碍了该领域的发展,也阻碍了将其发展为生物医学专家可用的应用。这项工作提出了一种从生物医学文献中挖掘过去发现的方法。该方法利用发现所产生的影响,使用描述性统计来检测某一关系在不同时期的流行程度。该方法的有效性根据代表最先进的 "时间切片 "方法的基线进行了测试。这种方法可以收集大量有时间戳的发现。这些发现可用于枸杞多糖评估,从而缓解长期以来评估不足的问题。它还可以为更精细的 LBD 方法铺平道路,利用这些过去发现的多样性来训练监督模型。最后,该数据集(或受我们方法启发的未来版本)可用作系统性综述的方法论工具。我们从这个角度提供了一个在线探索工具,网址是 https://brainmend.adaptcentre.ie/ 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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