Evidence map›Paper›PMID 37533767›Full record

ReviewMedComm2023

Applications of multi-omics analysis in human diseases.

Chongyang Chen, Jing Wang, Donghui Pan, Xinyu Wang, Yuping Xu, Junjie Yan, Lizhen Wang, Xifei Yang, Min Yang, Gong-Ping Liu

Open access · goldAbstract readReview
In one paragraph

Review in MedComm, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 302 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
302citing papers in PubMed, 4 pooled it
65.7field-weighted citation impact, top 1% 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

302 citing papers in PubMed, 4 syntheses or guidelines pooled it, 427 citations in OpenAlex.

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242 more citing papers are in PubMed but not listed here.

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 3 institutions in 3 countries.

Chongyang ChenKey Laboratory of Nuclear Medicine Ministry of Health Jiangsu Key Laboratory of Molecular Nuclear Medicine Jiangsu Institute of Nuclear Medicine Wuxi China.
Jing WangShenzhen Key Laboratory of Modern Toxicology Shenzhen Medical Key Discipline of Health Toxicology (2020-2024) Shenzhen Center for Disease Control and Prevention Shenzhen China.
Donghui PanKey Laboratory of Nuclear Medicine Ministry of Health Jiangsu Key Laboratory of Molecular Nuclear Medicine Jiangsu Institute of Nuclear Medicine Wuxi China.
Xinyu WangKey Laboratory of Nuclear Medicine Ministry of Health Jiangsu Key Laboratory of Molecular Nuclear Medicine Jiangsu Institute of Nuclear Medicine Wuxi China.
Yuping XuKey Laboratory of Nuclear Medicine Ministry of Health Jiangsu Key Laboratory of Molecular Nuclear Medicine Jiangsu Institute of Nuclear Medicine Wuxi China.
Junjie YanKey Laboratory of Nuclear Medicine Ministry of Health Jiangsu Key Laboratory of Molecular Nuclear Medicine Jiangsu Institute of Nuclear Medicine Wuxi China.
Lizhen WangKey Laboratory of Nuclear Medicine Ministry of Health Jiangsu Key Laboratory of Molecular Nuclear Medicine Jiangsu Institute of Nuclear Medicine Wuxi China.
Xifei YangShenzhen Key Laboratory of Modern Toxicology Shenzhen Medical Key Discipline of Health Toxicology (2020-2024) Shenzhen Center for Disease Control and Prevention Shenzhen China.ORCID https://orcid.org/0000-0002-9000-7016
Min YangKey Laboratory of Nuclear Medicine Ministry of Health Jiangsu Key Laboratory of Molecular Nuclear Medicine Jiangsu Institute of Nuclear Medicine Wuxi China.
Gong-Ping LiuCo-innovation Center of Neurodegeneration Nantong University Nantong China.
Ministry Of Health · FJNantong University · CNShenzhen Center for Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Indexed as

biomarkermachine learning and deep learningmulti‐omicsneurodegenerative diseaseprecision medicine

Identifiers

PMID37533767
PMCPMC10390758
OpenAlexW4385459199

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.