Causal Network Analysis of Omics Data Using Prior Knowledge Databases
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| Title: | Causal Network Analysis of Omics Data Using Prior Knowledge Databases |
|---|---|
| Authors: | Svinin, Gleb, GLAAB, Enrico |
| Contributors: | Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) |
| Source: | Briefings in Bioinformatics, in press (2025) |
| Publication Year: | 2025 |
| Collection: | University of Luxembourg: ORBilu - Open Repository and Bibliography |
| Subject Terms: | causal analysis, causal reasoning, molecular networks, prior knowledge, systems biology, bioinformatics workflows, Life sciences, Biotechnology, Human health sciences, Sciences du vivant, Biotechnologie, Sciences de la santé humaine |
| Description: | peer reviewed ; 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. ; U-AGR-7611 - C24/BM/18865990/AsynIntact - GLAAB Enrico ; 3. Good health and well-being |
| Document Type: | article in journal/newspaper |
| Language: | English |
| ISSN: | 1467-5463 1477-4054 |
| Relation: | info:eu-repo/grantAgreement/EC/H2020/825575; 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; urn:issn:1467-5463; urn:issn:1477-4054; https://orbilu.uni.lu/handle/10993/66380; info:hdl:10993/66380 |
| Availability: | https://orbilu.uni.lu/handle/10993/66380 |
| Rights: | restricted access ; http://purl.org/coar/access_right/c_16ec ; info:eu-repo/semantics/restrictedAccess |
| Accession Number: | edsbas.31F7F878 |
| Database: | BASE |
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Nájsť tento článok vo Web of Science | Abstract: | peer reviewed ; 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. ; U-AGR-7611 - C24/BM/18865990/AsynIntact - GLAAB Enrico ; 3. Good health and well-being |
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| ISSN: | 14675463 14774054 |