Data Mining

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Topics: Gene
Data Mining And Statistical Approaches In Identifying Contrasting Trends In Reactome And Biocarta

By
Sumayya Iqbal
SP09-BSB-036

Zainab Khan
SP09-BSB-045
BS Thesis (Feb 2009-Jan 2013)
COMSATS Institute of Information Technology
Islamabad- Pakistan
January, 2013
COMSATS Institute of Information Technology

Data Mining And Statistical Approaches In Identifying Contrasting Trends In Reactome And Biocarta
A Thesis Presented to COMSATS Institute of Information Technology, Islamabad
In Partial Fulfilment
Of the requirement for the Degree of

B.S. (Bioinformatics)

By

Sumayya Iqbal
CIIT/ SP09-BSB-036/ISB
Zainab Khan
CIIT/ SP09-BSB-45/ISB
January, 2013

Data Mining And Statistical Approaches In Identifying Contrasting Trends In Reactome And Biocarta

An Undergraduate Thesis submitted to the Department of Bioscience as partial fulfillment of the requirement for the award of the Degree of B.S. (Bioinformatics).

Name | Registration Number | Sumayya IqbalZainab Khan | CIIT/SP09-BSB-036/ISB CIIT/SP09 -BSB-045/ISB |

Supervisors
Dr. Rani Faryal
Mr. Syed Shujaat Ali Zaidi
Department of Biosciences,
CIIT, Islamabad Campus.
January, 2013

Final Approval
This thesis titled
Data Mining And Statistical Approaches In Identifying Contrasting Trends In Reactome And Biocarta
Submitted for the Degree of BS Bioinformatics by Sumayya Iqbal Zaianb Khan
Has been approved for the COMSATS Institute of information Technology Islamabad
External Examiner: __________________________________________

Supervisor: _____________________________________________
Dr. Rani Faryal

Co-Supervisor: ________________________________________________
Mr. Syed Shujaat Ali Zaidi

Head of Department/ Chairman: ________________________________________________
Dr. Syed Habib Bokhari
Associate Professor

Declaration
We Sumayya Iqbal and



References: 2. Anna Bauer-Mehren, L. I. (2009). Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Molecular systems biology, 5(1). 3. Berg JM, T. J. (2002). Glycolysis and gluconeogenesis. New York: W H Freeman. 4. Blenis, P. P. (2004). ERK and p38 MAPK-Activated Protein Kinases: a Family of Protein Kinases with Diverse Biological Functions. 68, 320-344. 5. Bonetta, L. (2010, December 8). Protein–protein interactions: Interactome under construction. Nature, 851-854. 6. Cates, S. (2012, 12 12). GENE. Retrieved 11 22, 2012, from NCBI: National Center for Biotechnology Information: http://www.ncbi.nlm.nih.gov/gene/7316 7 8. Cooper, G. M. (2000). Cell Sigalling. Sunderland (MA): Sinauer Associates, Inc. 9. Cork, J. M. (2004). The evolution of molecular genetic pathways and networks. Bioessays, 26(5, 479-484. 10. Corné H Verhees, S. W. (2003). The unique features of glycolytic pathways in Archaea. The unique features of glycolytic pathways in Archaea. Biochemical Journal,, 375(Pt 2), 231. 11. Daniel A Beard, S.-d. L. (2002). Energy balance for analysis of complex metabolic networks. Biophysical journal, 83(1), 79. 12. Domazet-Lošo, D. T. (2011). The evolutionary origin of orphan genes. Nature Reviews Genetics, 12(10), 692-702. 13. Ganesh A. Viswanathan, J. S. (2008). Getting Started in Biological Pathway Construction and Analysis. PLoS computational biology, 4(2), e16. 14. Glynn Dennis Jr, B. T. (2003, August 14). DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biology . 15. Gomez, L. (2008). G72/G30 (DAOA) and juvenile-onset mood disorders. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 150(7),, 1007-1012. 16. Graveley, B. R. (2001). Alternative splicing: increasingdiversity in the proteomic world. Science direct, 17(2), 100-107. 17. Guang-Zhong Wang, W.-H. C. (2011). Coexpression of Linked Gene Pairs Persists Long after Their Separation. 18. Harrtmink, A. J. (2005, May). Reverse Engineering gene regulatory networks. Nature, 23(5). 19. Holland SK, B. C. (1987). Proteins, exons and molecular evolution. Biosystems,, 20(2), 181-206. 20. Hongwu Ma1, A. S. (2007). The Edinburgh human metabolic network reconstruction and its functional analysis. 21. Isabelle Wolowczuk, C. V. (2008). Feeding Our Immune System: Impact on Metabolism. Clinical and Developmental Immunology. 22. Jeffrey D Orth, T. M. (2011). A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011. Molecular systems biology, 7(1). 23. Jiaqi Shi, Y. F. (2003). The p34cdc2-related Cyclin-dependent kinase 11 Interacts with the p47 Subunit of Eukaryotic Initiation Factor 3 during Apoptosis. Journal of Biological Chemistry, 278.7(2003), 5062-5071. 24. Jing He, K. W. (2011). Gene-based interaction analysis by incorporating external linkage disequilibrium information. European Journal of Human Genetics, 19(2), 164-172. 25. Joshi-Tope G, G. M. (2005). Reactome: a knowledgebase of biological pathways. 26. Karin, M. (1994). Signal transduction from the cell surface to the nucleus through the phosphorylation of transcription factors. 415-424. 27. Lal, A. (1999). A Public Database for Gene Expression in Human Cancers. Cancer Research, 59(21), 5403-5407. 28. Leonard Guarente & Cynthia Kenyon. (2000, November). Genetic pathways that regulate ageing in model organisms. 408. 29. Lisa Matthews, G. G. (2008). Reactome knowledgebase of human biological pathways and processes. Nucleic acids research, 37(suppl 1), D619-D622. 30. Lissette Gomez, K. W. (2008). G72/G30 (DAOA) and juvenile-onset mood disorders. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics,, 150(7), 1007-1012. 31. Lobo, I. (2008). Environmental Influences on Gene Expression. Nature Education, 1(1). 32. M. P. Kurhekar, S. A. (2001). Genome wide pathway analysis and visualization using gene expression data. Proc PSB '02, 462-473. 33. Manyuan Long, E. B. (2003). The Origin Of New Genes:Glimpses From The Young. Nature Reviews Genetics,, 4(11, 865-875. 34. Mengel-From J, B. C. (2010). Human eye colour and HERC2, OCA2 and MATP. Forensic Sci. Int. Genet,, 4, 323-328. 35. Ming Gua. (2010). Biological Pathways A pathway to explore diseases mechanisms. 36. Moss, V. S. (2006, april 1). Regulation of rRNA Synthesis in Human and Mouse Cells is Not Determined by Changes in Active Gene Count. cell cycle, 5(7), 735 - 739. 37. Murcray, C. E. (2008). Gene-Environment Interaction in Genome-Wide Association Studies. American journal of epidemiology,, 169(2), 219-226. 38. Nishimura, D. (2001, June). BioCarta. Biotech Software & Internet Report. The Computer Software Journal for Scient,, 2(3), 117-120. 39. Q. Ma, P. L.-K. (2005). Identification of Significant Association and Gene-Gene Interaction of GABA Receptor Subunit Genes in Autism. American journal of human genetics, 77(3), 377. 40. Robin Haw and Lincoln Stein. (2012, June). Using the Reactome Database. Current Protocols in Bioinformatics, 8-7. 41. Robin Haw, H. H. (2011, 6 sep). Reactome pathway analysis to enrich biological discovery in proteomics data sets. proteomics, 11(18), 3598–3613. 42. SABiosciences A QIAGEN COMPANY. (n.d.). Retrieved 12 22, 2012, from +http://www.sabiosciences.com/rt_pcr_product/HTML/PAHS-012Z.html 43 44. Saraiya, P. (2005). Visualizing Biological Pathways: Requirements Analysis, Systems Evaluation and Research Agenda.  Information Visualization, 4(3), 191-205. 45. Schilling CH. (2000). Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective.  Journal of theoretical biology, 203(3), 229 46 47. Sook S. Ha, I. K. (2011). Applications of Different Weighting Schemes to Improve Pathway-Based Analysis. Hindaw, Volume 2011 (2011), 15 . 48. Stefan M. Pulst, M. (1999). Genetic Linkage Analysis. Archieves Neurology, 56, 667-672. 49. Stein, L. D. (2003). Integrating biological databases. Nature Reviews Genetics, 4(5), 337-345. 50. Sternberg, W. F. (1995). Genetic Networks. Science (New York, N.Y.), 269(5224). 51. Tong Ihn Lee, N. J.-J. (2002). Transcriptional Regulatory Networks in Saccharomyces cerevisiae. Science Signalling, 298(5594), 799. 52. Victor Stefanovsky and Tom Moss . (2006, april 1). Regulation of rRNA Synthesis in Human and Mouse Cells is Not Determined by Changes in Active Gene Count. Landes Biosience, 5(7). 53. Yang, Y. (1988). The human genes for GM-CSF and IL 3 are closely linked in tandem on chromosome 5. Blood, 71(4), 958-961. 54. ZHENG LI and CHRISTINA CHAN. (2004, Februar 6). Inferring pathways and networks with a Bayesian framework. The FASEB Journal, 18(6), 746-748.

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