Causal Network Analysis of Omics Data Using Prior Knowledge Databases

[en] Identifying causal relationships in omics data is essential for understanding underlying biological processes. However, detecting causal relationships remains challenging due to the complexity of molecular networks and observational data limitations. To guide researchers, we conducted a systematic literature review of data-driven causal omics analysis methods that use structured prior knowledge from regulatory and interaction databases. We highlight how they differ in their use of this knowledge and the biological hypotheses they generate, and we discuss the strengths, limitations, and representative use cases of each approach. Finally, we address general limitations and outline future research directions. This review serves as a practical guide for the entire analysis process, from selecting prior knowledge databases to choosing and applying causal analysis methods for different research questions.

Research center :

Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)

Disciplines :

Life sciences: Multidisciplinary, general & others
Biotechnology
Human health sciences: Multidisciplinary, general & others

Author, co-author :

Svinin, Gleb

GLAAB, Enrico ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science

External co-authors :

Language :

English

Title :

Causal Network Analysis of Omics Data Using Prior Knowledge Databases

Publication date :

2025

Journal title :

Briefings in Bioinformatics

ISSN :

1467-5463

eISSN :

1477-4054

Pages :

in press

Peer reviewed :

Peer Reviewed verified by ORBi

Focus Area :

Systems Biomedicine

Development Goals :

3. Good health and well-being

European Projects :

H2020 - 825575 - EJP RD - European Joint Programme on Rare Diseases

FnR Project :

FNR17999421 - AD-PLCG2 - Towards Druggable Targets In Alzheimer’S Disease Through Characterization Of Plcg2-related Pathways In Neurons And Microglia, 2023 (01/07/2024-30/06/2027) - Enrico Glaab

Name of the research project :

U-AGR-7611 - C24/BM/18865990/AsynIntact - GLAAB Enrico

Funders :

FNR - Fonds National de la Recherche
European Union

Commentary :

In press

Available on ORBilu :

since 14 November 2025

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