- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Annual Issues
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 676328, 9 pages
Reducing the Complexity of Complex Gene Coexpression Networks by Coupling Multiweighted Labeling with Topological Analysis
1Department of Controls and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy
2Consorzio Interuniversitario Nazionale per l’Informatica, 11029 Verres, Italy
3Department of Agriculture, Forest and Food Sciences, Università degli Studi di Torino, 10124 Torino, Italy
Received 30 April 2013; Accepted 25 July 2013
Academic Editor: Sarah H. Elsea
Copyright © 2013 Alfredo Benso et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- A. L. Barabási and Z. N. Oltvai, “Network biology: understanding the cell’s functional organization,” Nature Reviews Genetics, vol. 5, no. 2, pp. 101–113, 2004.
- W. Huber, V. J. Carey, L. Long, S. Falcon, and R. Gentleman, “Graphs in molecular biology,” BMC Bioinformatics, vol. 8, no. 6, article S8, 2007.
- E. Pieroni, S. de la Fuente van Bentem, G. Mancosu, E. Capobianco, H. Hirt, and A. de la Fuente, “Protein networking: Insights into global functional organization of proteomes,” Proteomics, vol. 8, no. 4, pp. 799–816, 2008.
- J. Li, X. Hua, M. Haubrock, J. Wang, and E. Wingender, “The architecture of the gene regulatory networks of different tissues,” Bioinformatics, vol. 28, no. 18, pp. i509–i514, 2012.
- O. Kuchaiev and N. Pržulj, “Integrative network alignment reveals large regions of global network similarity in yeast and human,” Bioinformatics, vol. 27, no. 10, pp. 1390–1396, 2011.
- E. Prifti, J. D. Zucker, K. Clément, and C. Henegar, “Interactional and functional centrality in transcriptional co-expression networks,” Bioinformatics, vol. 26, no. 24, pp. 3083–3089, 2010.
- A. Benso, S. Di Carlo, G. Politano, and L. Sterpone, “Differential gene expression graphs: a data structure for classification in DNA Microarrays,” in Proceedings of the 8th IEEE International Conference on BioInformatics and BioEngineering (BIBE '08), pp. 1–6, Athens, Greece, October 2008.
- A. Benso, S. Di Carlo, G. Politano, and L. Sterpone, “A graph-based representation of Gene Expression profiles in DNA microarrays,” in Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB '08), pp. 75–82, Sun Valley, Idaho, USA, September 2008.
- A. Benso, S. Di Carlo, and G. Politano, “A cDNA microarray gene expression data classifier for clinical diagnostics based on graph theory,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 3, pp. 577–591, 2011.
- L. Chen, J. Xuan, R. B. Riggins, Y. Wang, and R. Clarke, “Identifying protein interaction subnetworks by a bagging markov random field-based method,” Nucleic Acids Research, vol. 41, no. 2article e42, 2013.
- S. Gatti, C. Leo, S. Gallo et al., “Gene expression profiling of HGF/Met activation in neonatal mouse heart,” Transgenic Research, vol. 22, no. 3, pp. 579–593, 2013.
- I. Lee, B. Ambaru, P. Thakkar, E. M. Marcotte, and S. Y. Rhee, “Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana,” Nature Biotechnology, vol. 28, no. 2, pp. 149–156, 2010.
- A. P. Gabow, S. M. Leach, W. A. Baumgartner, L. E. Hunter, and D. S. Goldberg, “Improving protein function prediction methods with integrated literature data,” BMC Bioinformatics, vol. 9, article 198, 2008.
- R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley-Interscience, New York, NY, USA, 2nd edition, 2001.
- G. Cong, K. L. Tan, A. K. H. Tung, and X. Xu, “Mining top-k covering rule groups for gene expression data,” in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD '05), F. Özcan, Ed., pp. 670–681, June 2005.
- X. Cui, H. Zhao, and J. Wilson, “Optimized ranking and selection methods for feature selection with application in microarray experiments,” Journal of Biopharmaceutical Statistics, vol. 20, no. 2, pp. 223–239, 2010.
- I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002.
- H. Mamitsuka, “Selecting features in microarray classification using ROC curves,” Pattern Recognition, vol. 39, no. 12, pp. 2393–2404, 2006.
- W. Pan, “A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments,” Bioinformatics, vol. 18, no. 4, pp. 546–554, 2002.
- C. Zhang, X. Lu, and X. Zhang, “Significance of gene ranking for classification of microarray samples,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. 3, pp. 312–320, 2006.
- Y. Saeys, I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507–2517, 2007.
- L. Yu and H. Liu, “Efficient feature selection via analysis of relevance and redundancy,” Journal of Machine Learning Research, vol. 5, pp. 1205–1224, 2004.
- L. Yu and H. Liu, “Feature selection for high-dimensional data: a fast correlation-based filter solution,” in Proceedings of the 20th International Conference on Machine Learning, pp. 856–863, August 2003.
- I. Inza, P. Larrañaga, R. Blanco, and A. J. Cerrolaza, “Filter versus wrapper gene selection approaches in DNA microarray domains,” Artificial Intelligence in Medicine, vol. 31, no. 2, pp. 91–103, 2004.
- Biogital Valley, ProteinQuest, 2013, http://www.proteinquest.com/.
- M. K. Kerr, M. Martin, and G. A. Churchill, “Analysis of variance for gene expression microarray data,” Journal of Computational Biology, vol. 7, no. 6, pp. 819–837, 2001.
- C. Cheadle, M. P. Vawter, W. J. Freed, and K. G. Becker, “Analysis of microarray data using Z score transformation,” Journal of Molecular Diagnostics, vol. 5, no. 2, pp. 73–81, 2003.
- S. Kaplan, A. Bren, E. Dekel, and U. Alon, “The incoherent feed-forward loop can generate non-monotonic input functions for genes,” Molecular Systems Biology, vol. 4, article 203, 2008.
- J. Macía, S. Widder, and R. Solé, “Specialized or flexible feed-forward loop motifs: a question of topology,” BMC Systems Biology, vol. 3, article 84, 2009.
- X. J. Tian, X. P. Zhang, F. Liu, and W. Wang, “Interlinking positive and negative feedback loops creates a tunable motif in gene regulatory networks,” Physical Review E, vol. 80, no. 1, part 1, Article ID 011926, 8 pages, 2009.
- F. Fioravanti, M. Helmer-Citterich, and E. Nardelli, “Modeling gene regulatory network motifs using statecharts,” BMC Bioinformatics, vol. 13, supplement 4, article S20, 2012.
- A. S. Konagurthu and A. M. Lesk, “On the origin of distribution patterns of motifs in biological networks,” BMC Systems Biology, vol. 2, article 73, 2008.
- J. F. Knabe, C. L. Nehaniv, and M. J. Schilstra, “Do motifs reflect evolved function? No convergent evolution of genetic regulatory network subgraph topologies,” BioSystems, vol. 94, no. 1-2, pp. 68–74, 2008.
- M. Muller, M. Obeyesekere, G. B. Mills, and P. T. Ram, “Network topology determines dynamics of the mammalian MAPK1,2 signaling network: Bifan motif regulation of C-Raf and B-Raf isoforms by FGFR and MC1R,” FASEB Journal, vol. 22, no. 5, pp. 1393–1403, 2008.
- L. Bullinger, K. Döhner, E. Bair et al., “Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia,” The New England Journal of Medicine, vol. 350, no. 16, pp. 1605–1616, 2004.
- J. R. Pollack, T. Sørlie, C. M. Perou et al., “Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 20, pp. 12963–12968, 2002.
- A. A. Alizadeh, M. B. Elsen, R. E. Davis et al., “Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling,” Nature, vol. 403, no. 6769, pp. 503–511, 2000.
- C. Palmer, M. Diehn, A. A. Alizadeh, and P. O. Brown, “Cell-type specific gene expression profiles of leukocytes in human peripheral blood,” BMC Genomics, vol. 7, article 115, 2006.
- Stanford University, “cDNA Stanford’s Microarray database,” 2013, http://genome-www.stanford.edu/.
- M. Natale, D. Bonino, P. Consoli et al., “A meta-analysis of two-dimensional electrophoresis pattern of the Parkinson's disease-related protein DJ-1,” Bioinformatics, vol. 26, no. 7, pp. 946–952, 2010.
- B. Vogelstein, D. Lane, and A. J. Levine, “Surfing the p53 network,” Nature, vol. 408, no. 6810, pp. 307–310, 2000.