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Cited by 545Bioinformatics and Genomic NetworksHealth, Environment, Cognitive AgingSingle-cell and spatial transcriptomics

Applications of multi‐omics analysis in human diseases

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Chen, Chongyang, Wang, Jing, Pan, Donghui, Wang, Xinyu, Xu, Yuping, Yan, Junjie, Wang, Lizhen, Yang, Xifei +2 more·2023·MedComm
DOI 10.1002/mco2.315PMC PMC10390758
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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.

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  • Multi-omics technologies enable comprehensive profiling of biological changes across multiple molecular layers in AD.

    “Recent advances in multi-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and microbiomics, enable comprehensive profiling of biological changes across multiple molecular layers in AD3.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [3]

    The section on applications in neurodegenerative diseases says that integrating genomic, transcriptomic, proteomic and metabolomic data can provide a more comprehensive understanding of AD pathophysiology. It does not include microbiomics among the approaches it discusses for AD, so that example in the citing sentence is not supported by these passages. (LLM, conf=0.82)

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Authors
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G.
Affiliations
  • Nantong University (CN)
  • Shenzhen Center for Disease Control and Prevention (CN)
  • Key Laboratory of Nuclear Medicine Ministry of Health Jiangsu Key Laboratory of Molecular Nuclear Medicine Jiangsu Institute of Nuclear Medicine Wuxi China
Corresponding author
  • Yang, Xifei
  • Yang, Min
  • Liu, Gong‐Ping
ORCID
Venue
MedComm
Publisher
Wiley
Published
2023-07-31
Volume
4
Issue
4
Pages
e315-e315
ISSN
2688-2663 (ISSN-L)
DOI
10.1002/mco2.315
PMC
PMC10390758
OpenAlex
W4385459199
Cited by
545
References
38
Citations by year
2026: 203 · 2025: 229 · 2024: 106 · 2023: 7
FWCI
14.39
Type
journal article
Language
en
Open access
Licence
Locations
  • MedComm (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (82071221, 82271474)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
multi-omics, multi-omics integration, machine learning, deep learning, single-cell multi-omics, spatial multi-omics, cancer, neurodegenerative diseases, aging, drug target identification, precision medicine, experimental design

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