DOI: 10.5281/zenodo.1035252
title: A global network of biomedical relationships derived from text

description: 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 <strong>September 15, 2019 version of PubTator</strong>. 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. ------------------------------------------------------------------------------------<br> REFERENCES Percha B, Altman RBA (2017) A global network of biomedical relationships derived from text. <em>Bioinformatics, </em>34(15): 2614-2624.<br> Percha B, Altman RBA (2015) Learning the structure of biomedical relationships from unstructured text. <em>PLoS Computational Biology,</em> 11(7): e1004216. This project depends on named entity annotations from the PubTator project:<br> https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/ Reference:<br> 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 (<strong>version 3.9.1</strong>):<br> https://stanfordnlp.github.io/CoreNLP/index.html Reference:<br> 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. ------------------------------------------------------------------------------------<br> THEMES <strong>chemical-gene</strong><br> (A+) agonism, activation<br> (A-) antagonism, blocking<br> (B) binding, ligand (esp. receptors)<br> (E+) increases expression/production<br> (E-) decreases expression/production<br> (E) affects expression/production (neutral)<br> (N) inhibits <strong>gene-chemical</strong><br> (O) transport, channels<br> (K) metabolism, pharmacokinetics<br> (Z) enzyme activity <strong>chemical-disease</strong><br> (T) treatment/therapy (including investigatory)<br> (C) inhibits cell growth (esp. cancers)<br> (Sa) side effect/adverse event<br> (Pr) prevents, suppresses<br> (Pa) alleviates, reduces<br> (J) role in disease pathogenesis <strong>disease-chemical</strong><br> (Mp) biomarkers (of disease progression) <strong>gene-disease</strong><br> (U) causal mutations<br> (Ud) mutations affecting disease course<br> (D) drug targets<br> (J) role in pathogenesis<br> (Te) possible therapeutic effect<br> (Y) polymorphisms alter risk<br> (G) promotes progression <strong>disease-gene</strong><br> (Md) biomarkers (diagnostic)<br> (X) overexpression in disease<br> (L) improper regulation linked to disease <strong>gene-gene</strong><br> (B) binding, ligand (esp. receptors)<br> (W) enhances response<br> (V+) activates, stimulates<br> (E+) increases expression/production<br> (E) affects expression/production (neutral)<br> (I) signaling pathway<br> (H) same protein or complex<br> (Rg) regulation<br> (Q) production by cell population ------------------------------------------------------------------------------------<br> 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 <em>Bioinformatics</em> manuscript, above).