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Connecting Seed Lists of Mammalian Proteins Using Steiner Trees
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- Department of Computational Biology and Molecular Biophysics, Rutgers, The State University of New Jersey
- Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York
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- Document Type:
- Manuscript
- Date:
- Received 05 April 2008 20:54 UTC; Posted 07 April 2008
- Subjects:
- Biotechnology, Cancer, Molecular Cell Biology, Bioinformatics
- Abstract:
Multivariate experiments and genomics studies applied to mammalian cells often produce lists of genes or proteins altered under treatment/disease vs. control/normal conditions. Such lists can be identified in known protein-protein interaction networks to produce subnetworks that “connect” the genes or proteins from the lists. Such subnetworks are valuable for biologists since they can suggest regulatory mechanisms that are altered under different conditions. Often such subnetworks are overloaded with links and nodes resulting in connectivity diagrams that are illegible due to edge overlap. In this study, we attempt to address this problem by implementing an approximation to the Steiner Tree problem to connect seed lists of mammalian proteins/genes using literature-based protein-protein interaction networks. To avoid over-representation of hubs in the resultant Steiner Trees we assign a cost to Steiner Vertices based on their connectivity degree. We applied the algorithm to lists of genes commonly mutated in colorectal cancer to demonstrate the usefulness of this approach.
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- License:
- This document is licensed to the public under the Creative Commons Attribution 3.0 License
- How to cite this document:
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White, Amelia and Ma’ayan, Avi. Connecting Seed Lists of Mammalian Proteins Using Steiner Trees. Available from Nature Precedings <http://hdl.handle.net/10101/npre.2008.1768.1> (2008)
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