Title: A global network of biomedical relationships derived from text

Type Dataset Percha, Bethany, Altman, Russ B. (2018): A global network of biomedical relationships derived from text. Zenodo. Dataset. https://zenodo.org/record/1134693

Authors: Percha, Bethany (Icahn School of Medicine at Mount Sinai) ; Altman, Russ B. (Stanford University) ;

Links

• Item record in Zenodo
• Digital object URL

Summary

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:

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 November 13, 2017 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. (Submitted to Bioinformatics; currently in revision.) 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. doi: 10.1093/nar/gkt44

Dependency parsing was provided by the Stanford CoreNLP toolkit: 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

More information

• DOI: 10.5281/zenodo.1134693

Subjects

• natural language processing, Medline, text mining, relation extraction, unsupervised learning

Dates

• Publication date: 2018
• Issued: January 09, 2018

Rights

• https://creativecommons.org/licenses/by/4.0/legalcode Creative Commons Attribution 4.0 International
• info:eu-repo/semantics/openAccess Open Access

Format

electronic resource

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