A global network of biomedical relationships derived from text

Name: A global network of biomedical relationships derived from text
Creator: Zenodo
Published: 2019-09-24T23:01:39+08:00

Zenodo2020-07-30 更新2026-05-28 收录

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This repository contains labeled, weighted networks of chemical-gene, gene-gene, gene-disease, and chemical-disease relationships based on single sentences in PubMed abstracts. All raw dependency paths are provided in addition to the labeled relationships. PART I: Connects dependency paths to labels, or "themes". Each record contains a dependency path followed by its score for each theme, and indicators of whether or not the path is part of the flagship path set for each theme (meaning that it was manually reviewed and determined to reflect that theme). The themes themselves are listed below and are in our paper (reference below). PART II: Connects sentences to dependency paths. It consists of sentences and associated metadata, entity pairs found in the sentences, and dependency paths connecting those entity pairs. Each record contains the following information: PubMed ID Sentence number (0 = title) First entity name, formatted First entity name, location (characters from start of abstract) Second entity name, formatted Second entity name, location First entity name, raw string Second entity name, raw string First entity name, database ID(s) Second entity name, database ID(s) First entity type (Chemical, Gene, Disease) Second entity type (Chemical, Gene, Disease) Dependency path Sentence, tokenized The "with-themes.txt" files only contain dependency paths with corresponding theme assignments from Part I. The plain ".txt" files contain all dependency paths. This release contains the annotated network for the September 15, 2019 version of PubTator. The version discussed in our paper, below, is an older one - from April 30, 2016. If you're interested in that network, it can be found in Version 1 of this repository. We will be releasing updated networks periodically, as the PubTator community continues to release new versions of named entity annotations for Medline each month or so. ------------------------------------------------------------------------------------ REFERENCES Percha B, Altman RBA (2017) A global network of biomedical relationships derived from text. Bioinformatics, 34(15): 2614-2624. Percha B, Altman RBA (2015) Learning the structure of biomedical relationships from unstructured text. PLoS Computational Biology, 11(7): e1004216. This project depends on named entity annotations from the PubTator project: https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/ Reference: Wei CH et. al., PubTator: a Web-based text mining tool for assisting Biocuration, Nucleic acids research, 2013, 41 (W1): W518-W522. Dependency parsing was provided by the Stanford CoreNLP toolkit (version 3.9.1): https://stanfordnlp.github.io/CoreNLP/index.html Reference: Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60. ------------------------------------------------------------------------------------ THEMES chemical-gene (A+) agonism, activation (A-) antagonism, blocking (B) binding, ligand (esp. receptors) (E+) increases expression/production (E-) decreases expression/production (E) affects expression/production (neutral) (N) inhibits gene-chemical (O) transport, channels (K) metabolism, pharmacokinetics (Z) enzyme activity chemical-disease (T) treatment/therapy (including investigatory) (C) inhibits cell growth (esp. cancers) (Sa) side effect/adverse event (Pr) prevents, suppresses (Pa) alleviates, reduces (J) role in disease pathogenesis disease-chemical (Mp) biomarkers (of disease progression) gene-disease (U) causal mutations (Ud) mutations affecting disease course (D) drug targets (J) role in pathogenesis (Te) possible therapeutic effect (Y) polymorphisms alter risk (G) promotes progression disease-gene (Md) biomarkers (diagnostic) (X) overexpression in disease (L) improper regulation linked to disease gene-gene (B) binding, ligand (esp. receptors) (W) enhances response (V+) activates, stimulates (E+) increases expression/production (E) affects expression/production (neutral) (I) signaling pathway (H) same protein or complex (Rg) regulation (Q) production by cell population ------------------------------------------------------------------------------------ FORMATTING NOTE A few users have mentioned that the dependency paths in the "part-i" files are all lowercase text, whereas those in the "part-ii" files maintain the case of the original sentence. This complicates mapping between the two sets of files. We kept the part-ii files in the same case as the original sentence to facilitate downstream debugging - it's easier to tell which words in a particular sentence are contributing to the dependency path if their original case is maintained. When working with the part-ii "with-themes" files, if you simply convert the dependency path to lowercase, it is guaranteed to match to one of the paths in the corresponding part-i file and you'll be able to get the theme scores. Apologies for the additional complexity, and please reach out to us if you have any questions (see correspondence information in the Bioinformatics manuscript, above).

本仓库收录基于PubMed摘要中单句构建的标记加权网络，涵盖化学-基因、基因-基因、基因-疾病及化学-疾病四类生物关联关系。除已标记的关联对外，本仓库同时提供所有原始依存路径。 ### 第一部分：依存路径与主题关联本部分用于建立依存路径与标签（又称“主题”）的映射关系。每条记录包含一条依存路径，以及该路径在各主题下的得分，同时标注该路径是否属于对应主题的旗舰路径集（即经人工审核并确认可准确反映该主题的路径）。完整主题列表详见下文及本研究论文（参考文献见文末）。 ### 第二部分：句子与依存路径关联本部分用于建立句子与依存路径的映射关系，包含句子及相关元数据、句子中出现的实体对，以及连接这些实体对的依存路径。每条记录包含以下内容：PubMed编号、句子序号（0表示标题）、格式化第一实体名称、第一实体位置（相对于摘要起始的字符数）、格式化第二实体名称、第二实体位置、第一实体原始字符串、第二实体原始字符串、第一实体数据库标识符、第二实体数据库标识符、第一实体类型（化学、基因、疾病）、第二实体类型（化学、基因、疾病）、依存路径、分词后的句子。仅收录带主题分配依存路径的"with-themes.txt"文件，仅包含第一部分中已分配主题的依存路径；普通".txt"文件则收录所有原始依存路径。本数据集基于**2019年9月15日版PubTator**标注构建。本研究论文中提及的旧版网络基于2016年4月30日的PubTator版本，可在本仓库的V1版本中获取。我们将定期随PubTator社区每月更新的Medline命名实体注释版本，发布更新后的关联网络。 ------------------------------------------------------------------------------------ ### 参考文献 Percha B, Altman RBA (2017) 基于文本构建的全球生物医学关联网络. *Bioinformatics（生物信息学）*, 34(15): 2614-2624. Percha B, Altman RBA (2015) 从非结构化文本中学习生物医学关联的结构. *PLOS Computational Biology（PLOS计算生物学）*, 11(7): e1004216. 本项目依赖PubTator项目的命名实体注释，来源网址： https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/ 参考文献： Wei CH 等. PubTator：一款用于辅助生物标注的Web文本挖掘工具. *Nucleic Acids Research（核酸研究）*, 2013, 41(W1): W518-W522. 依存句法分析由Stanford CoreNLP工具包（版本3.9.1）提供，来源网址： https://stanfordnlp.github.io/CoreNLP/index.html 参考文献： Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit. 见：第52届国际计算语言学协会年会论文集：系统演示环节, 第55-60页. ------------------------------------------------------------------------------------ ### 主题列表 #### 化学-基因 (A+) 激动作用、激活 (A-) 拮抗作用、阻断 (B) 结合、配体（尤指受体） (E+) 表达/产生上调 (E-) 表达/产生下调 (E) 表达/产生影响（中性） (N) 抑制 #### 基因-化学 (O) 转运、通道作用 (K) 代谢、药代动力学 (Z) 酶活性 #### 化学-疾病 (T) 治疗/疗法（含临床试验性治疗） (C) 抑制细胞生长（尤指癌症） (Sa) 副作用/不良事件 (Pr) 预防、抑制 (Pa) 缓解、减轻 (J) 在疾病发病机制中的作用 #### 疾病-化学 (Mp) 疾病进展生物标志物 #### 基因-疾病 (U) 致病性突变 (Ud) 影响疾病进程的突变 (D) 药物靶点 (J) 在发病机制中的作用 (Te) 潜在治疗效应 (Y) 多态性改变疾病风险 (G) 促进疾病进展 #### 疾病-基因 (Md) 诊断用生物标志物 (X) 疾病中过表达 (L) 异常调控与疾病相关 #### 基因-基因 (B) 结合、配体（尤指受体） (W) 增强应答 (V+) 激活、刺激 (E+) 表达/产生上调 (E) 表达/产生影响（中性） (I) 信号通路 (H) 同一蛋白质或复合物 (Rg) 调控作用 (Q) 细胞群体产生 ------------------------------------------------------------------------------------ ### 格式说明部分用户反馈，"part-i"文件中的依存路径均为小写文本，而"part-ii"文件中的依存路径保留了原句的大小写格式，这会给两类文件间的映射带来不便。我们保留"part-ii"文件的原始大小写格式，以方便下游调试：保留原始大小写可更直观地判断原句中的哪些词汇对依存路径有贡献。若使用"part-ii"的"with-themes"文件，只需将依存路径转换为小写，即可确保与对应"part-i"文件中的路径匹配，并获取主题得分。为此带来的额外复杂度敬请谅解，如有任何疑问，请与我们联系（详见上述《Bioinformatics》论文中的通讯信息）。

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Zenodo

创建时间：

2019-09-24

A global network of biomedical relationships derived from text

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