• Causal network analysis of omi...

Treffer: Causal network analysis of omics data using prior knowledge databases.

Title:
Causal network analysis of omics data using prior knowledge databases.
Authors:
Svinin G; Biomedical Data Science Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg., Glaab E; Biomedical Data Science Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.
Source:
Briefings in bioinformatics [Brief Bioinform] 2025 Nov 01; Vol. 26 (6).
Publication Type:
Journal Article; Systematic Review
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
Imprint Name(s):
Publication: Oxford : Oxford University Press
Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
MeSH Terms:
Computational Biology*/methods , Genomics*/methods , Gene Regulatory Networks* , Databases, Factual*, Humans ; Databases, Genetic
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Grant Information:
C24/BM/18865990/AsynIntact Luxembourg National Research Fund; INTER/JPND23/17999421/AD-PLCG2 Luxembourg National Research Fund; INTER/22/17104370/RECAST Luxembourg National Research Fund; INTER/EJP RD22/17027921/PreDYT Luxembourg National Research Fund
Contributed Indexing:
Keywords: bioinformatics workflows; causal reasoning; molecular networks; network analysis; prior knowledge; systems biology
Entry Date(s):
Date Created: 20251205 Date Completed: 20251205 Latest Revision: 20251218
Update Code:
20251219
PubMed Central ID:
PMC12703490
DOI:
10.1093/bib/bbaf654
PMID:
41348604
Database:
MEDLINE

Weitere Informationen

Identifying causal relationships in omics data is essential for understanding underlying biological processes. However, detecting these 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 grouped methods into three approaches based on the extent of prior knowledge integration: regulon-level (direct regulator-target links, straightforward interpretation, but with the risk of oversimplification), flow-level (multi-step propagation from regulators to targets, broader mechanism explanation, but lacking uncertainty modeling), and network-level (system-wide interactions and crosstalk, most comprehensive, but with increased computational complexity and requiring particularly careful interpretation). These methods have demonstrated utility across diverse applications, including identification of therapeutic targets in acute myeloid leukemia, elucidation of mechanisms in IgA nephropathy, and detection of regulatory perturbations in Alzheimer's disease. We discuss the strengths, limitations, and representative use cases of each approach, and 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 (PKDBs) to choosing and applying causal analysis methods for different research questions.
(© The Author(s) 2025. Published by Oxford University Press.)