Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Mining patterns in disease classification forests
Journal of Biomedical Informatics
An iterative parameter estimation method for biological systems
Proceedings of the 3rd international workshop on Emerging computational methods for the life sciences
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Motivation: Given the complex nature of biological systems, pathways often need to function in a coordinated fashion in order to produce appropriate physiological responses to both internal and external stimuli. Therefore, understanding the interaction and crosstalk between pathways is important for understanding the function of both cells and more complex systems. Results: We have developed a computational approach to detect crosstalk among pathways based on protein interactions between the pathway components. We built a global mammalian pathway crosstalk network that includes 580 pathways (covering 4753 genes) with 1815 edges between pathways. This crosstalk network follows a power-law distribution: P(k) ~ k−γ, γ = 1.45, where P(k) is the number of pathways with k neighbors, thus pathway interactions may exhibit the same scale-free phenomenon that has been documented for protein interaction networks. We further used this network to understand colorectal cancer progression to metastasis based on transcriptomic data. Contact: yong.2.li@gsk.com Supplementary information: Supplementary data are available at Bioinformatics online.