- 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 304029, 8 pages
Prediction and Analysis of Retinoblastoma Related Genes through Gene Ontology and KEGG
1Department of Ophthalmology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
2Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
3State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Jiaotong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
4College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
5Department of Ophthalmology, Shanghai First People’s Hospital, Shanghai Jiaotong University, Shanghai 200080, China
6Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York City, NY 10029, USA
Received 17 June 2013; Accepted 16 July 2013
Academic Editor: Yudong Cai
Copyright © 2013 Zhen Li 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.
- F. Di Nicolantonio, M. Neale, Z. Onadim, J. L. Hungerford, J. L. Kingston, and I. A. Cree, “The chemosensitivity profile of retinoblastoma,” Recent Results in Cancer Research, vol. 161, pp. 73–80, 2003.
- A. MacCarthy, J. M. Birch, G. J. Draper et al., “Retinoblastoma in Great Britain 1963–2002,” British Journal of Ophthalmology, vol. 93, no. 1, pp. 33–37, 2009.
- G. M. Gordon and W. Du, “Conserved RB functions in development and tumor suppression,” Protein and Cell, vol. 2, no. 11, pp. 864–878, 2011.
- D. Lohmann, “Retinoblastoma,” Advances in Experimental Medicine and Biology, vol. 685, pp. 220–227, 2010.
- D. W. Felsher, “Role of MYCN in retinoblastoma,” The Lancet Oncology, vol. 14, pp. 270–271, 2013.
- A. Schüler, S. Weber, M. Neuhäuser et al., “Age at diagnosis of isolated unilateral retinoblastoma does not distinguish patients with and without a constitutional RB1 gene mutation but is influenced by a parent-of-origin effect,” European Journal of Cancer, vol. 41, no. 5, pp. 735–740, 2005.
- D. Rushlow, B. Piovesan, K. Zhang et al., “Detection of mosaic RB1 mutations in families with retinoblastoma,” Human Mutation, vol. 30, no. 5, pp. 842–851, 2009.
- S. Richter, K. Vandezande, N. Chen et al., “Sensitive and efficient detection of RB1 gene mutations enhances care for families with retinoblastoma,” American Journal of Human Genetics, vol. 72, no. 2, pp. 253–269, 2003.
- L. Hood, J. R. Heath, M. E. Phelps, and B. Lin, “Systems biology and new technologies enable predictive and preventative medicine,” Science, vol. 306, no. 5696, pp. 640–643, 2004.
- S. S. Knox, “From 'omics' to complex disease: a systems biology approach to gene-environment interactions in cancer,” Cancer Cell International, vol. 10, article 11, 2010.
- J. J. Hornberg, F. J. Bruggeman, H. V. Westerhoff, and J. Lankelma, “Cancer: a systems biology disease,” BioSystems, vol. 83, no. 2-3, pp. 81–90, 2006.
- D. Altshuler, M. J. Daly, and E. S. Lander, “Genetic mapping in human disease,” Science, vol. 322, no. 5903, pp. 881–888, 2008.
- E. Camon, D. Barrell, V. Lee, E. Dimmer, and R. Apweiler, “The Gene Ontology Annotation (GOA) database—an integrated resource of GO annotations to the UniProt knowledgebase,” In Silico Biology, vol. 4, no. 1, pp. 5–6, 2004.
- L. Li, K. Zhang, J. Lee, S. Cordes, D. P. Davis, and Z. Tang, “Discovering cancer genes by integrating network and functional properties,” BMC Medical Genomics, vol. 2, article 61, 2009.
- S. Chakraborty, S. Khare, S. K. Dorairaj, V. C. Prabhakaran, D. R. Prakash, and A. Kumar, “Identification of genes associated with tumorigenesis of retinoblastoma by microarray analysis,” Genomics, vol. 90, no. 3, pp. 344–353, 2007.
- A. Ganguly and C. L. Shields, “Differential gene expression profile of retinoblastoma compared to normal retina,” Molecular Vision, vol. 16, pp. 1292–1303, 2010.
- R. J. Kinsella, A. Kähäri, S. Haider et al., “Ensembl BioMarts: a hub for data retrieval across taxonomic space,” Database, vol. 2011, article bar030, 2011.
- Z. He, T. Huang, X. Shi et al., “Computational analysis of protein tyrosine nitration,” in Proceedings of the 4th International Conference on Computational Systems Biology (ISB '10), pp. 35–42, 2010.
- P. Carmona-Saez, M. Chagoyen, F. Tirado, J. M. Carazo, and A. Pascual-Montano, “GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists,” Genome Biology, vol. 8, no. 1, article R3, 2007.
- T. Huang, L. Chen, Y.-D. Cai, and K.-C. Chou, “Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property,” PLoS One, vol. 6, no. 9, Article ID e25297, 2011.
- B.-Q. Li, J. Zhang, T. Huang, L. Zhang, and Y.-D. Cai, “Identification of retinoblastoma related genes with shortest path in a protein-protein interaction network,” Biochimie, vol. 94, pp. 1910–1917, 2012.
- D. Szklarczyk, A. Franceschini, M. Kuhn et al., “The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored,” Nucleic Acids Research, vol. 39, no. 1, pp. D561–D568, 2011.
- H. Cramér, Mathematical Methods of Statistics, Princeton University Press, Princeton, NJ, USA, 1946.
- M. Kendall and A. Stuart, The Advanced Theory of Statistics, vol. 2, Inference and Relationship, Macmillan, New York, NY, USA, 1979.
- K. M. Harrison, T. Kajese, H. I. Hall, and R. Song, “Risk factor redistribution of the national HIV/AIDS surveillance data: an alternative approach,” Public Health Reports, vol. 123, no. 5, pp. 618–627, 2008.
- H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005.
- L.-L. Zheng, S. Niu, P. Hao, K. Feng, Y.-D. Cai, and Y. Li, “Prediction of protein modification sites of pyrrolidone carboxylic acid using mRMR feature selection and analysis,” PLoS One, vol. 6, no. 12, Article ID e28221, 2011.
- Y.-F. Gao, B. Li -Q, Y.-D. Cai, K.-Y. Feng, Z.-D. Li, and Y. Jiang, “Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection,” Molecular BioSystems, vol. 9, pp. 61–69, 2013.
- B.-Q. Li, Y.-D. Cai, K.-Y. Feng, and G.-J. Zhao, “Prediction of protein cleavage site with feature selection by random forest,” PLoS One, vol. 7, Article ID e45854, 2012.
- N. Zhang, B.-Q. Li, S. Gao, J.-S. Ruan, and Y.-D. Cai, “Computational prediction and analysis of protein [gamma]-carboxylation sites based on a random forest method,” Molecular BioSystems, vol. 8, pp. 2946–2955, 2012.
- B.-Q. Li, K.-Y. Feng, L. Chen, T. Huang, and Y.-D. Cai, “Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS,” PLoS One, vol. 7, Article ID e43927, 2012.
- K. M. Ting and I. H. Witten, “Stacking bagged and dagged models,” in Proceedings of the 14th international Conference on Machine Learning, pp. 367–375, San Francisco, Calif, USA, 1997.
- I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2005.
- P. K. Srimani and M. S. Koti, “A Comparison of different learning models used in data mining for medical data,” in Proceedings of the 2nd International Conference on Methods and Models in Science and Technology (ICM2ST '11), pp. 51–55, India, November 2011.
- L. Chen, L. Lu, K. Feng et al., “Multiple classifier integration for the prediction of protein structural classes,” Journal of Computational Chemistry, vol. 30, no. 14, pp. 2248–2254, 2009.
- Y.-D. Cai, L. Lu, L. Chen, and J.-F. He, “Predicting subcellular location of proteins using integrated-algorithm method,” Molecular Diversity, vol. 14, no. 3, pp. 551–558, 2010.
- C. Peng, L. Liu, B. Niu et al., “Prediction of RNA-binding proteins by voting systems,” BioMed Research International, vol. 2011, Article ID 506205, 8 pages, 2011.
- R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI '95), vol. 2, pp. 1137–1145, 1995.
- B.-Q. Li, T. Huang, J. Zhang et al., “An ensemble prognostic model for colorectal cancer,” PLoS One, vol. 8, Article ID e63494, 2013.
- B.-Q. Li, T. Huang, L. Liu, Y.-D. Cai, and K.-C. Chou, “Identification of colorectal cancer related genes with mrmr and shortest path in protein-protein interaction network,” PLoS One, vol. 7, no. 4, Article ID e33393, 2012.
- E. P. Lamber, F. Beuron, E. P. Morris, D. I. Svergun, and S. Mittnacht, “Structural insights into the mechanism of phosphoregulation of the retinoblastoma protein,” PLoS One, vol. 8, Article ID e58463, 2013.
- M. M. Kandalam, M. Beta, U. K. Maheswari, S. Swaminathan, and S. Krishnakumar, “Oncogenic microRNA 17-92 cluster is regulated by epithelial cell adhesion molecule and could be a potential therapeutic target in retinoblastoma,” Molecular Vision, vol. 18, pp. 2279–2287, 2012.
- A. Loewer, E. Batchelor, G. Gaglia, and G. Lahav, “Basal dynamics of p53 reveal transcriptionally attenuated pulses in cycling cells,” Cell, vol. 142, no. 1, pp. 89–100, 2010.
- C. Dai and W. Gu, “P53 post-translational modification: deregulated in tumorigenesis,” Trends in Molecular Medicine, vol. 16, no. 11, pp. 528–536, 2010.
- S. Huang, Z. Zhu, Y. Wang et al., “Tet1 is required for Rb phosphorylation during G1/S phase transition,” Biochemical and Biophysical Research Communications, vol. 434, no. 2, pp. 241–244, 2013.
- B. F. Clem and J. Chesney, “Molecular pathways: regulation of metabolism by RB,” Clinical Cancer Research, vol. 18, pp. 6096–6100, 2012.
- G. Ciavarra and E. Zacksenhaus, “Rescue of myogenic defects in Rb-deficient cells by inhibition of autophagy or by hypoxia-induced glycolytic shift,” Journal of Cell Biology, vol. 191, no. 2, pp. 291–301, 2010.
- G. Ciavarra and E. Zacksenhaus, “Multiple pathways counteract cell death induced by RB1 loss: implications for cancer,” Cell Cycle, vol. 10, no. 10, pp. 1533–1539, 2011.