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Issue No.03 - March (2012 vol.45)
pp: 31-38
Mehmet Koyuturk , Case Western Reserve University
ABSTRACT
Recent developments in biotechnology have enabled interrogation of the cell at various levels, leading to many types of "omic" data that provide valuable information on multiple genetic and environmental factors and their interactions. The featured Web extra is a video interview with Mehmet Koyutürk of Case Western Reserve about how biotechnology can track genetic markers to advance cancer research.
INDEX TERMS
computational biology, genetics, bioinformatics, databases, mining methods and algorithms, graph algorithms, information theory
CITATION
Mehmet Koyuturk, "Using Protein Interaction Networks to Understand Complex Diseases", Computer, vol.45, no. 3, pp. 31-38, March 2012, doi:10.1109/MC.2012.40
REFERENCES
1. W.G. Hatfield, S.P. Hung, and P. Baldi, "Differential Analysis of DNA Microarray Gene Expression Data," Molecular Microbiology, vol. 47, no. 4, 2003, pp. 871-877.
2. D.F. Ransohoff, "Rules of Evidence for Cancer Molecular-Marker Discovery and Validation," Nature Reviews Cancer, vol. 4, no. 4, 2004, pp. 309-314.
3. E.E. Schadt, "Molecular Networks as Sensors and Drivers of Common Human Diseases," Nature, vol. 461, no. 7261, 2009, pp. 218-223.
4. E.E. Eichler et al., "Missing Heritability and Strategies for Finding the Underlying Causes of Complex Disease," Nature Reviews Genetics, vol. 11, no. 6, 2010, pp. 446-450.
5. D.K. Slonim, "From Patterns to Pathways: Gene Expression Data Analysis Comes of Age," Nature Genetics, vol. 32 (suppl.), 2002, pp. 502-508.
6. A. Subramanian et al., "Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles," Proc. Nat'l Academy Sciences, vol. 102, no. 43, 2005, pp. 15545-15550.
7. M. Koyutürk, "Algorithmic and Analytical Methods in Network Biology," WIREs Systems Biology Medicine, vol. 2, no. 3, 2010, pp. 277-292.
8. I. Lee et al., "A Probabilistic Functional Network of Yeast Genes," Science, vol. 306, no. 5701, 2004, pp. 1555-1558.
9. T.S. Keshava et al., "Human Protein Reference Database–2009 Update," Nucleic Acids Research, vol. 37, 2009, D767-D772.
10. C. Stark et al., "The BioGRID Interaction Database: 2011 Update," Nucleic Acids Research, vol. 39 (Database issue), 2011, D698-D704.
11. A. Zelezniak et al., "Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes," PLoS Computational Biology, vol. 6, no. 4, 2010, e1000729.
12. D.R. Rhodes and A.M. Chinnaiyan, "Integrative Analysis of the Cancer Transcriptome," Nature Genetics, vol. 37, 2005, S31-S37.
13. G. Gibson, "Decanalization and the Origin of Complex Disease," Nature Reviews Genetics, vol. 10, no. 2, 2009, pp. 134-140.
14. P. Dao et al., "Inferring Cancer Subnetwork Markers Using Density-Constrained Biclustering," Bioinformatics, vol. 26, 2010, pp. i625-i631.
15. T. Ideker et al., "Discovering Regulatory and Signalling Circuits in Molecular Interaction Networks," Bioinformatics, vol. 18 (suppl), 2001, pp. 233-240.
16. H.-Y. Chuang et al., "Network-Based Classification of Breast Cancer Metastasis," Molecular Systems Biology, vol. 3, 2007; www.ncbi.nlm.nih.gov/pmc/articlesPMC2063581.
17. S.A. Chowdhury and M. Koyutürk, "Identification of Coordinately Dysregulated Subnetworks in Complex Phenotypes," Proc. Pacific Symp. Biocomputing, 2010; http://psb.stanford.edu/psb-online/proceedings/ psb10chowdhury.pdf.
18. I. Ulitsky, R.M. Karp, and R. Shamir, "Detecting Disease-Specific Dysregulated Pathways via Analysis of Clinical Expression Profiles," Proc. Ann. Int'l Conf. Research Computational Molecular Biology (RECOMB 08), LNCS 4955, Springer, 2008, pp. 347-359.
19. D. Anastassiou, "Computational Analysis of the Synergy among Multiple Interacting Genes," Molecular Systems Biology, vol. 3, 2007, article no. 83; www.nature.com/msb/journal/v3/n1/fullmsb4100124.html .
20. J. Watkinson et al., "Identification of Gene Interactions Associated with Disease from Gene Expression Data Using Synergy Networks," BMC Systems Biology, 2008; www.biomedcentral.com/1752-0509/210.
21. S.A. Chowdhury et al., "Subnetwork State Functions Define Dysregulated Subnetworks in Cancer," J. Computational Biology, vol. 18, no. 3, 2011, pp. 263-281.
22. N. Reinmuth et al., "Alphabeta3 Integrin Antagonist S247 Decreases Colon Cancer Metastasis and Angiogenesis and Improves Survival in Mice," Cancer Research, vol. 63, no. 9, 2003, pp. 2079-2087.
23. P. Dao et al., "Optimally Discriminative Subnetwork Markers Predict Response to Chemotherapy," Bioinformatics, vol. 27, no. 13, 2011, pp. i205-i213.
24. J. Dutkowski and T. Ideker, "Protein Networks as Logic Functions in Development and Cancer," PLoS Computational Biology, vol. 7, no. 9, 2011, e1002180, 09.
25. Y.-A. Kim, S. Wuchty, and T.M. Przytycka, "Identifying Causal Genes and Dysregulated Pathways in Complex Diseases," PLoS Computational Biology, vol. 7, no. 3, 2011, e1001095.
26. R.K. Nibbe, M. Koyutürk, and M.R. Chance, "An Integrative -omics Approach to Identify Functional Subnetworks in Human Colorectal Cancer," PLoS Computational Biology, vol. 6, no. 1, 2010, e1000639.
27. E.E. Schadt, S.H. Friend, and D.A. Shaywitz, "A Network View of Disease and Compound Screening," Nature Reviews Drug Discovery, vol. 8, no. 4, 2009, pp. 286-295.
28. O. Vanunu et al., "Associating Genes and Protein Complexes with Disease via Network Propagation," PLoS Computational Biology, vol. 6, no. 1, 2010, e1000641.
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