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Methods for the integration of multi-omics data: mathematical aspects.

Background Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem. Results and conclusions We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.

Methods for the integration of multi-omics data: mathematical aspects.

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

书目信息

  • 引用:Bersanelli M, Mosca E, Remondini D, Giampieri E, Sala C, Castellani G, Milanesi L. (2016). Methods for the integration of multi-omics data: mathematical aspects. BMC bioinformatics. PMID 26821531 · PMC4959355 · DOI 10.1186/s12859-015-0857-9
  • 证据类型:METHODS
  • 主题:multi-omics
  • 被引次数(采集时):261
  • 原始记录:[Europe PMC](https://europepmc.org/article/MED/26821531)
  • 来源许可:[CC-BY](https://creativecommons.org/licenses/by/)
  • 作者摘要(按来源许可复用)

    Background Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem. Results and conclusions We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.

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