Public knowledge document

Multi-omics Data Integration, Interpretation, and Its Application.

To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

Multi-omics Data Integration, Interpretation, and Its Application.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

  • 引用:Subramanian I, Verma S, Kumar S, Jere A, Anamika K. (2020). Multi-omics Data Integration, Interpretation, and Its Application. Bioinformatics and biology insights. PMID 32076369 · PMC7003173 · DOI 10.1177/1177932219899051
  • 证据类型:REVIEW
  • 主题:multi-omics
  • 被引次数(采集时):937
  • 原始记录:[Europe PMC](https://europepmc.org/article/MED/32076369)
  • 来源许可:[CC-BY](https://creativecommons.org/licenses/by/)
  • 作者摘要(按来源许可复用)

    To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

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    Multi-omics Data Integration, Interpretation, and Its Application. · GeniOmics