ArticleBMC genomics2012
Discovering pathway cross-talks based on functional relations between pathways.
Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Dynamic Factor Analysis for Sparse and Irregular Longitudinal Data: An Application to Metabolite Measurements in a COVID-19 Study.Statistics in medicine · 2026Article
- Harnessing network pharmacology in drug discovery: an integrated approach.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer.Cancer medicine · 2024Article
- Article
- Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions.Nucleic acids research · 2024Article
- PINTnet: construction of condition-specific pathway interaction network by computing shortest paths on weighted PPI.BMC systems biology · 2017Article
- Toward Personalized Network Biomarkers in Alzheimer's Disease: Computing Individualized Genomic and Protein Crosstalk Maps.Frontiers in aging neuroscience · 2017Article
- Xtalk: a path-based approach for identifying crosstalk between signaling pathways.Bioinformatics (Oxford, England) · 2016Article
- Advances in translational bioinformatics and population genomics in the Asia-Pacific.BMC genomics · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations.
resultsThis study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis.
conclusionsThis function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations.
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