Evidence map›Paper›PMID 37237656›Full record

ArticleBioengineering (Basel, Switzerland)2023

Biomedical Relation Extraction Using Dependency Graph and Decoder-Enhanced Transformer Model.

Seonho Kim, Juntae Yoon, Ohyoung Kwon

Open access · goldAbstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.2field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Seonho KimDepartment of Computer Science and Engineering, Sogang University, Seoul 04107, Republic of Korea.
Juntae YoonVAIV Company, Seoul 04107, Republic of Korea.
Ohyoung KwonDepartment of Future Technology, Korea University of Technology and Education, Cheonan-si 31253, Republic of Korea.
Korea University of Technology and Education · KRSogang University · KR

Funding

Korea Foundation NRF-2020R1I1A1A01073125
6 · The paper itself

Abstract

The identification of drug-drug and chemical-protein interactions is essential for understanding unpredictable changes in the pharmacological effects of drugs and mechanisms of diseases and developing therapeutic drugs. In this study, we extract drug-related interactions from the DDI (Drug-Drug Interaction) Extraction-2013 Shared Task dataset and the BioCreative ChemProt (Chemical-Protein) dataset using various transfer transformers. We propose BERT

Indexed as

ChemProtCPR (chemical–protein relation)DDI (drug–drug interaction)GAT (graph-attention network)relation extractionself-attentionT5 (text-to-text transfer transformer)transformer

Identifiers

PMID37237656
PMCPMC10215465
OpenAlexW4378515217

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.