Article

RENET2: High-Performance Full-text Gene-Disease Relation Extraction with Iterative Training Data Expansion

2021-03-19

Abstract excerpt

<h4>Background</h4> Relation extraction is a fundamental task for extracting gene-disease associations from biomedical text. Existing tools have limited capacity, as they can extract gene-disease associations only from single sentences or abstract texts. <h4>Results</h4> In this work, we propose RENET2, a deep learning-based relation extraction method, which implements section filtering and ambiguous relations m...

Topics

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

Identifiers and source

Literature Corpus work: 4e1ac79c-3733-5a34-9a8d-d3ae9684873f
DOI: 10.1101/2021.03.18.436005

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

RENET2: High-Performance Full-text Gene-Disease Relation Extraction with Iterative Training Data ExpansionDOI 10.1101/2021.03.18.436005Read

RDscan: Extracting RNA-disease relationship from literature Using pre-training modelSemantic proximity Read

Beyond associations: A benchmark Causal Relation Extraction Dataset (CRED) of disease-causing genes, its comparative evaluation, interpretation and applicationSemantic proximity Read

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

GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learningSemantic proximity Read

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

Improving Biomedical Named Entity Recognition with Label Re-correction and Knowledge DistillationSemantic proximity Read

Predicting non-coding RNA function using Artificial Intelligence.Semantic proximity Read

Relation Extraction for Diet, Non-Communicable Disease and Biomarker Associations (RECoDe): A CoDiet studySemantic proximity Read

Accurate Name Entity Recognition for Biomedical Literatures: A Combined High-quality Manual Annotation and Deep-learning Natural Language Processing StudySemantic proximity Read

A Novel Automated Approach to Mutation-Cancer Relation Extraction by Incorporating Heterogeneous Knowledge.Semantic proximity Read

Europe PMC Annotated Full-text Corpus for Gene/Proteins, Diseases and OrganismsSemantic proximity Read

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