Evidence map›Paper›PMID 27570661›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2016

Semantic Web Ontology and Data Integration: a Case Study in Aiding Psychiatric Drug Repurposing.

Chen Liang, Jingchun Sun, Cui Tao

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chen LiangThe University of Texas Health Science Center, Houston, TX.
Jingchun SunThe University of Texas Health Science Center, Houston, TX.
Cui TaoThe University of Texas Health Science Center, Houston, TX.

Funding

Patient Medical History Representation, Extraction, and Inference from EHR DataR01LM011829 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI · 2014 to 2018
$1.7M
NLM NIH HHS R01 LM011829
6 · The paper itself

Abstract

Despite ongoing progress towards treating mental illness, there remain significant difficulties in selecting probable candidate drugs from the existing database. We describe an ontology - oriented approach aims to represent the nexus between genes, drugs, phenotypes, symptoms, and diseases from multiple information sources. Along with this approach, we report a case study in which we attempted to explore the candidate drugs that effective for both bipolar disorder and epilepsy. We constructed an ontology that incorporates the knowledge between the two diseases and performed semantic reasoning task on the ontology. The reasoning results suggested 48 candidate drugs that hold promise for a further breakthrough. The evaluation was performed and demonstrated the validity of the proposed ontology. The overarching goal of this research is to build a framework of ontology - based data integration underpinning psychiatric drug repurposing. This approach prioritizes the candidate drugs that have potential associations among genes, phenotypes and symptoms, and thus facilitates the data integration and drug repurposing in psychiatric disorders.

Identifiers

PMID27570661
PMCPMC5001753

What OpenQuestion holds

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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.