DILS '08 Proceedings of the 5th international workshop on Data Integration in the Life Sciences
Deciphering Drug Action and Escape Pathways: An Example on Nasopharyngeal Carcinoma
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
DILS '09 Proceedings of the 6th International Workshop on Data Integration in the Life Sciences
Predicting disease phenotypes based on the molecular networks with Condition-Responsive Correlation
International Journal of Data Mining and Bioinformatics
Computational Biology and Chemistry
Subnetwork state functions define dysregulated subnetworks in cancer
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
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Motivation: Current high-throughput protein–protein interaction (PPI) data do not provide information about the condition(s) under which the interactions occur. Thus, the identification of condition-responsive PPI sub-networks is of great importance for investigating how a living cell adapts to changing environments. Results: In this article, we propose a novel edge-based scoring and searching approach to extract a PPI sub-network responsive to conditions related to some investigated gene expression profiles. Using this approach, what we constructed is a sub-network connected by the selected edges (interactions), instead of only a set of vertices (proteins) as in previous works. Furthermore, we suggest a systematic approach to evaluate the biological relevance of the identified responsive sub-network by its ability of capturing condition-relevant functional modules. We apply the proposed method to analyze a human prostate cancer dataset and a yeast cell cycle dataset. The results demonstrate that the edge-based method is able to efficiently capture relevant protein interaction behaviors under the investigated conditions. Contact: guoz@ems.hrbmu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.