Evidence map›Paper›PMID 35928229›Full record

ArticleFrontiers in molecular biosciences2022

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method.

Hao Li, Feiming Huang, Huiping Liao, Zhandong Li, Kaiyan Feng, Tao Huang, Yu-Dong Cai

Open access · goldAbstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 10% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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  13. UnravelingFrontiers in oncology · 2024
    Review
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  16. Article
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  18. Machine Learning and COVID-19: Lessons from SARS-CoV-2.Advances in experimental medicine and biology · 2023
    Article
  19. Article
  20. Article
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

7 authors at 5 institutions in 1 country.

Hao LiCollege of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China.
Feiming HuangSchool of Life Sciences, Shanghai University, Shanghai, China.
Huiping LiaoOphthalmology and Optometry Medical School, Shandong University of Traditional Chinese Medicine, Jinan, China.
Zhandong LiCollege of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China.
Kaiyan FengDepartment of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, China.
Tao HuangBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Yu-Dong CaiSchool of Life Sciences, Shanghai University, Shanghai, China.
Jilin Engineering Normal University · CNShanghai University · CNGuangdong Polytechnic Normal University · CNShandong University of Traditional Chinese Medicine · CNUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4

Indexed as

classification algorithmCOVID-19feature selectionimmune cellmachine learning

Identifiers

PMID35928229
PMCPMC9344575
OpenAlexW4285794000

What Socratic 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 Socratic graph.