Using uncertainty to link and rank evidence from biomedical literature for model curation.
Using uncertainty to link and rank evidence from biomedical literature for model curation.
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DOI：
10.1093/bioinformatics/btx466
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发表时间：
2017-12-01
期刊：
影响因子：
--
通讯作者：
Ananiadou S
中科院分区：
文献类型：
--
作者：
Zerva C;Batista-Navarro R;Day P;Ananiadou S
近年来，在提供文献证据的文本挖掘方法的帮助下，生物医学网络和模型的自动管理领域取得了很大进展。这样的方法不仅必须提取与模型交互相关的文本片段，而且还必须能够将证据上下文化，并为有问题的交互提供额外的置信度分数。尽管计算置信度分数的各种方法主要集中在提取信息的质量上，但很少有研究探索作者所传达的文本不确定性。尽管文本不确定性在生物医学文本挖掘中被认为是文本挖掘交互（事件）的一个属性，但作为一种为路径或其他生物医学模型中的交互提供置信度度量的手段，它的研究还远远不够。在这项工作中，我们专注于改进事件文本不确定性的识别，并探索如何将其用作生物医学模型的额外信心措施。我们提出了一种从文献中提取不确定性的新方法，使用结合规则归纳和机器学习的混合方法。然后讨论了这种混合方法的各种变体，以及它们的优缺点。我们运用主观逻辑理论，对同一交互作用从不同来源提取的多个不确定性值进行组合。我们的方法分别在BioNLP-ST和Genia-MK语料库上实现了0.76和0.88的f分数，比之前发表的工作有了很大的改进。此外，我们评估了我们提出的系统在两个不同领域相关的途径，即白血病和黑色素瘤癌症研究。使用的白血病途径模型可以在pathway Studio中获得，而Ras模型可以通过PathwayCommons获得。不确定度提取系统的在线演示可在http://argo.nactem.ac.uk/test上进行研究。相关代码可在https://github.com/c-zrv/uncertainty_components.git上获得。以上的详情可参阅补充资料。补充数据可在生物信息学网站获得。
In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models. We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research. The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material. Supplementary data are available at Bioinformatics online.
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DOI：
10.1109/icimw.2009.5324773
发表时间：
2009-01-01
期刊：
METHODS IN BIOENGINEERING: SYSTEMS ANALYSIS OF BIOLOGICAL NETWORKS
影响因子：
--
作者：
Kim, Jin-Hong;Asthagiri, Anand R.
通讯作者：
Asthagiri, Anand R.
影响因子：
4.3
作者：
Malhotra A;Younesi E;Gurulingappa H;Hofmann-Apitius M
通讯作者：
Hofmann-Apitius M
DOI：
10.1142/s0218488501000831
发表时间：
2001-06-01
影响因子：
1.5
作者：
Josang, A
通讯作者：
Josang, A
影响因子：
4
作者：
Ananiadou S;Thompson P;Nawaz R;McNaught J;Kell DB
通讯作者：
Kell DB
影响因子：
3
作者：
Donaldson, I;Martin, J;de Bruijn, B;Wolting, C;Lay, V;Tuekam, B;Zhang, SD;Baskin, B;Bader, GD;Michalickova, K;Pawson, T;Hogue, CWV
通讯作者：
Hogue, CWV