Evidence map›Paper›PMID 40932882›Full record

ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2021

Scientific Evidence Based Knowledge Graph in Rare Diseases.

Qian Zhu, Ruizheng Liu, Gunjan Vatas, Andrew Clough, Yanji Xu, Ðắc-Trung Nguyễn, Ewy Mathé, Eric Sid

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Qian ZhuDivision of Pre-Clinical Innovation, National Center for Advancing Translational Sciences, Rockville, USA.
Ruizheng LiuAxle Informatics, Inc, Rockville, USA.
Gunjan VatasGMG ArcData, LLC, Washington, USA.
Andrew CloughData Decode, LLC, Washington, USA.
Yanji XuOffice of Rare Disease Research, National Center for Advancing Translational Sciences, Besthasta, USA.
Ðắc-Trung NguyễnDivision of Pre-Clinical Innovation, National Center for Advancing Translational Sciences, Rockville, USA.
Ewy MathéDivision of Pre-Clinical Innovation, National Center for Advancing Translational Sciences, Rockville, USA.
Eric SidOffice of Rare Disease Research, National Center for Advancing Translational Sciences, Rockville, USA.

Funding

Intramural NIH HHS Z99 TR999999
6 · The paper itself

Abstract

Rare diseases are naturally associated with low prevalence rate, which raises a big challenge due to less data available for supporting preclinical and clinical studies. Therefore, it is critical to fully utilize the accumulated scientific publications in rare diseases over years, in order to access full spectrum of scientific research and enable relevant scientific evidence extraction and generation. In this study, we obtained rare disease related PubMed articles, extracted multiple types of biomedical information, and semantically presented the data in a knowledge graph, which is hosted in Neo4j based on a predefined data model to support further rare disease research.

Indexed as

Knowledge GraphNeo4jPubMedRare Diseases

Identifiers

PMID40932882
PMCPMC12419305

What OpenQuestion holds

Textmetadata
LicenceTDM
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.