Evidence map›Paper›PMID 35390003›Full record

ArticlePloS one2022

Predicted mouse interactome and network-based interpretation of differentially expressed genes.

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

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. 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, top 96% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

10 authors at 2 institutions in 1 country.

Hai-Bo ZhangInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China.
Xiao-Bao DingInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China.
Jie JinInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China.
Wen-Ping GuoInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China.
Qiao-Lei YangInstitute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China.
Peng-Cheng ChenInstitute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China.
Heng YaoInstitute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China.
Li RuanInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China.
Yu-Tian TaoInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China.ORCID 0000-0002-9360-0771
Xin ChenInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China.
Taizhou University · CNZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The house mouse or Mus musculus has become a premier mammalian model for genetic research due to its genetic and physiological similarities to humans. It brought mechanistic insights into numerous human diseases and has been routinely used to assess drug efficiency and toxicity, as well as to predict patient responses. To facilitate molecular mechanism studies in mouse, we present the Mouse Interactome Database (MID, Version 1), which includes 155,887 putative functional associations between mouse protein-coding genes inferred from functional association evidence integrated from 9 public databases. These putative functional associations are expected to cover 19.32% of all mouse protein interactions, and 26.02% of these function associations may represent protein interactions. On top of MID, we developed a gene set linkage analysis (GSLA) web tool to annotate potential functional impacts from observed differentially expressed genes. Two case studies show that the MID/GSLA system provided precise and informative annotations that other widely used gene set annotation tools, such as PANTHER and DAVID, did not. Both MID and GSLA are accessible through the website http://mouse.biomedtzc.cn.

Indexed as

Databases, GeneticMammalsAnimalsHumansMice

Identifiers

PMID35390003
PMCPMC8989236
OpenAlexW4225744390

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.