Evidence map›Paper›PMID 33216897›Full record

ArticleDatabase : the journal of biological databases and curation2020

Predicted rat interactome database and gene set linkage analysis.

Yu-Tian Tao, Xiao-Bao Ding, Jie Jin, Hai-Bo Zhang, Wen-Ping Guo, Li Ruan, Qiao-Lei Yang, Peng-Cheng Chen, Heng Yao, Xin Chen

Open access · goldAbstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.2field-weighted citation impact, top 49% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Yu-Tian TaoInstitute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China.
Xiao-Bao DingInstitute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China.
Jie JinInstitute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China.
Hai-Bo ZhangInstitute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China.
Wen-Ping GuoInstitute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China.
Li RuanInstitute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China.
Qiao-Lei YangInstitute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
Peng-Cheng ChenInstitute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
Heng YaoInstitute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
Xin ChenInstitute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China.
Taizhou University · CNZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rattus norvegicus, or the rat, has been widely used as animal models for a diversity of human diseases in the last 150 years. The rat, as a disease model, has the advantage of relatively large body size and highly similar physiology to humans. In drug discovery, rat models are routinely used in drug efficacy and toxicity assessments. To facilitate molecular pharmacology studies in rats, we present the predicted rat interactome database (PRID), which is a database of high-quality predicted functional gene interactions with balanced sensitivity and specificity. PRID integrates functional gene association data from 10 public databases and infers 305 939 putative functional associations, which are expected to include 13.02% of all rat protein interactions, and 52.59% of these function associations may represent protein interactions. This set of functional interactions may not only facilitate hypothesis formulation in molecular mechanism studies, but also serve as a reference interactome for users to perform gene set linkage analysis (GSLA), which is a web-based tool to infer the potential functional impacts of a set of changed genes observed in transcriptomics analyses. In a case study, we show that GSLA based on PRID may provide more precise and informative annotations for investigators to understand the physiological mechanisms underlying a phenotype and lead investigators to testable hypotheses for further studies. Widely used functional annotation tools such as Gene Ontology (GO) analysis, and Database for Annotation, Visualization and Integrated Discovery (DAVID) did not provide similar insights. Database URL: http://rat.biomedtzc.cn.

Indexed as

Databases, GeneticGene Expression ProfilingAnimalsGene OntologyRats

Identifiers

PMID33216897
PMCPMC7678787
OpenAlexW3105909779

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.