Article

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research

2014-07-24

Abstract excerpt

<h4>Background</h4> Current biomedical research needs to leverage and exploit the large amount of information reported in publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 9b018692-1033-538f-b121-8efeb3b6830b
DOI: 10.1101/007443

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational researchDOI 10.1101/007443Read

DISEASES 2.0: a weekly updated database of disease–gene associations from text mining and data integrationSemantic proximity Read

dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene InteractionsSemantic proximity Read

DISEASES: Text mining and data integration of disease–gene associationsSemantic proximity Read

DISEASES: text mining and data integration of disease-gene associations.Semantic proximity Read

dRiskKB: a large-scale disease-disease risk relationship knowledge base constructed from biomedical text.Semantic proximity Read

Extracting phenotypic information from the literature via natural language processing.Semantic proximity Read

OnTheFly <sup>2.0</sup> : a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysisSemantic proximity Read

Lit-OTAR Framework for Extracting Biological Evidences from LiteratureSemantic proximity Read

Ontology-driven association rule mining for biomedical entity relationships: integrating hierarchical knowledge to improve gene-disease discovery.Semantic proximity Read

RENET2: High-Performance Full-text Gene-Disease Relation Extraction with Iterative Training Data ExpansionSemantic proximity Read

Text mining of 15 million full-text scientific articlesSemantic proximity Read

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text miningSemantic proximity Read