There is a newer version of this record available.

Dataset Open Access

A global network of biomedical relationships derived from text

Percha, Bethany; Altman, Russ B.

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Percha, Bethany</dc:creator>
  <dc:creator>Altman, Russ B.</dc:creator>
  <dc: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 April 22, 2018 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.


Percha B, Altman RBA (2017) A global network of biomedical relationships derived from text. (In press at Bioinformatics.)
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:

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:

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.


(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

(O) transport, channels
(K) metabolism, pharmacokinetics
(Z) enzyme activity

(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

(Mp) biomarkers (of disease progression)

(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

(Md) biomarkers (diagnostic)
(X) overexpression in disease
(L) improper regulation linked to disease

(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</dc:description>
  <dc:subject>natural language processing</dc:subject>
  <dc:subject>text mining</dc:subject>
  <dc:subject>relation extraction</dc:subject>
  <dc:subject>unsupervised learning</dc:subject>
  <dc:title>A global network of biomedical relationships derived from text</dc:title>
All versions This version
Views 24,6991,681
Downloads 30,8395,971
Data volume 44.6 TB4.4 TB
Unique views 18,6351,414
Unique downloads 6,692281


Cite as