Evidence map›Paper›PMID 37497545›Full record

ArticleFrontiers in microbiology2023

COVIDanno, COVID-19 annotation in human.

Yuzhou Feng, Mengyuan Yang, Zhiwei Fan, Weiling Zhao, Pora Kim, Xiaobo Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in microbiology, 2023. 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

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.

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

6 authors.

Yuzhou FengWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Mengyuan YangSchool of Life Sciences, Zhengzhou University, Zhengzhou, China.
Zhiwei FanCenter for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.
Weiling ZhaoCenter for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.
Pora KimCenter for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.
Xiaobo ZhouCenter for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.

Funding

Functional annotation of new genes aided by deep learningR35GM138184 · NIGMS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KIM, PORA · 2020 to 2024
$1.7M
NIGMS NIH HHS R35 GM138184
6 · The paper itself

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 19 (COVID-19), has caused a global health crisis. Despite ongoing efforts to treat patients, there is no universal prevention or cure available. One of the feasible approaches will be identifying the key genes from SARS-CoV-2-infected cells. SARS-CoV-2-infected

Indexed as

COVID-19expressioninfection statein vitro modelsregulatory network

Identifiers

PMID37497545
PMCPMC10366449

What OpenQuestion holds

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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.