Applications of multi‐omics analysis in human diseases

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 d...

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Vydáno v:MedComm (2020) Ročník 4; číslo 4; s. e315 - n/a
Hlavní autoři: Chen, Chongyang, Wang, Jing, Pan, Donghui, Wang, Xinyu, Xu, Yuping, Yan, Junjie, Wang, Lizhen, Yang, Xifei, Yang, Min, Liu, Gong‐Ping
Médium: Journal Article
Jazyk:angličtina
Vydáno: China John Wiley & Sons, Inc 01.08.2023
John Wiley and Sons Inc
Wiley
Témata:
biomarker
Biomarkers
Genomics
Machine learning
machine learning and deep learning
multi‐omics
neurodegenerative disease
Precision medicine
Review
Reviews
precision medicine
multi‐omics
neurodegenerative disease
biomarker
machine learning and deep learning
ISSN:2688-2663, 2688-2663
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Popis
Shrnutí: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. Multi‐omics contains genomics, transcriptomics, proteomics, metabolomics, etc. And the experimental design considers the disease characteristics, disease model, sample size and phenotypic data. After the data integration by the approach of correlation, network and machining learning, the multi‐omics can be applied in diagnosis, biomarkers, targets of human disease and target discovery of natural compound.
Bibliografie:Chongyang Chen and Jing Wang contributed equally to this work.
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ISSN:2688-2663
2688-2663
DOI:10.1002/mco2.315