A review of causal discovery methods for molecular network analysis

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Background: With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions. Methods: We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade. Results: In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area. Conclusion: Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

Original languageEnglish
Article numbere2055
JournalMolecular Genetics and Genomic Medicine
Issue number10
Publication statusPublished - 1 Oct 2022


  • Bayesian networks
  • causal inference
  • causal molecular network
  • mendelian randomisation
  • omics


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