Details

Autor(en) / Beteiligte

• Kelly, Jack
• Berzuini, Carlo
• Keavney, Bernard
• Tomaszewski, Maciej
• Guo, Hui

Titel

A review of causal discovery methods for molecular network analysis

Ist Teil von

• Molecular genetics & genomic medicine, 2022-10, Vol.10 (10), p.e2055-n/a

Ort / Verlag

United States: John Wiley & Sons, Inc

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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. 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 and genetics. This paper aims to introduce and review the available methods for building large‐scale causal molecular networks that have been developed in the past decade. Additionally, current limitations to biological interpretation of these networks is discussed and important factors to consider for future studies on molecular networks.