LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19.

Q2 Agricultural and Biological Sciences Genomics and Informatics Pub Date : 2021-09-01 Epub Date: 2021-09-30 DOI:10.5808/gi.21013

Sizhuo Ouyang, Yuxing Wang, Kaiyin Zhou, Jingbo Xia

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Abstract

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.

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LitCovid-AGAC：基于 COVID-19 的细胞和分子水平注释数据集。

目前，冠状病毒病 2019（COVID-19）的文献急剧增加，文本量的增加使得进行大规模文本挖掘和知识发现成为可能。因此，如何对这些文本进行整理，以获取有关 COVID-19 机制的重要信息，成为生物医学自然语言处理（BioNLP）领域的一个关键问题。PubAnnotation 是一个对齐注释系统，它为生物馆员上传注释或合并其他外部注释提供了一个高效的平台。受整合多种有用 COVID-19 注释的启发，我们将三种注释资源合并到 LitCovid 数据集，并构建了交叉注释语料库 LitCovid-AGAC。该语料库包括 PubTator 中的 Mutation、Species、Gene、Disease，OGER 中的 GO、CHEBI，AGAC 中的 Var、MPA、CPA、NegReg、PosReg、Reg 等 12 个标签，以及 LitCovid 中的 50,018 篇 COVID-19 摘要。其中包含足够丰富的信息，有可能揭示 COVID-19 病理机制中隐藏的知识。

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