Call for Paper - July 2023 Edition
IJCA solicits original research papers for the July 2023 Edition. Last date of manuscript submission is June 20, 2023. Read More

Disease-Gene Association: Tools, Techniques and Trends

IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013
© 2013 by IJCA Journal
NCIPET2013 - Number 2
Year of Publication: 2013
Shital Patil
Satish Kumbhar

Shital Patil and Satish Kumbhar. Article: Disease-Gene Association: Tools, Techniques and Trends. IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013 NCIPET 2013(2):18-23, December 2013. Full text available. BibTeX

	author = {Shital Patil and Satish Kumbhar},
	title = {Article: Disease-Gene Association: Tools, Techniques and Trends},
	journal = {IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013},
	year = {2013},
	volume = {NCIPET 2013},
	number = {2},
	pages = {18-23},
	month = {December},
	note = {Full text available}


Genetic make-up of an individual is responsible for expression of external characters. Genes express through creation of intermediate products such as amino acids and proteins in turn. Proteins structurally and functionally are responsible for causing phenotype change. Some properties like mutations in gene may cause abnormalities. This makes it necessary to relate particular gene with the diseases it caused. Essence of gene and gene product information has proven its role in many aspects of life and disease-gene association is closely concerned with it. It has got the paramount importance in many fields like genetic engineering, forensics and clinical diagnosis. The availability of essential experimental data from various genetic and protein related databases aided by state of the art computing technology has brought unforeseen revolution in the field of bioinformatics. Efforts on human genome project and GWAS (Genome Wide Association Studies) have brought radical development in sequencing and assembly. Post genomic era demands processing and usage of this valuable data in real practice to address real life scenarios. Various data mining and statistical approaches exist to address disease-gene association problem. We describe the analysis of such tools and techniques based on parameters like working principles, algorithms or methods used, speciality and limitations etc. .


  • M. Y. Galperin, "The molecular biology database collection: 2006 update," Nucleic Acids Research, vol. 34, no. Database-Issue, 2006Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  • A. Hamosh, A. F. Scott, J. S. Amberger, C. A. Bocchini, andV. A. McKusick, "Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders," Nucleic AcidsResearch, vol. 33, no. Database-Issue, pp. 514–517, 2005. . Available: http://dx. doi. org/10. 1093/nar/gki033
  • K. D. Pruitt and D. R. Maglott, "Refseq and locuslink: NCBI gene centered resources," Nucleic Acids Research, vol. 29, no. 1, pp. 137–140, 2001. . Available http://dx. doi. org/10. 1093/nar/29. 1. 137
  • M. A. Harris and et al (!), "The gene ontology (GO) database and informatics resource," Nucleic Acids Res. , vol. 32, pp. D258–D261, Jan. 2004
  • M. A. van Driel, K. Cuelenaere, P. P. C. W. Kemmeren, J. A. M. Leunissen, H. G. Brunner, and G. Vriend, "Geneseeker: extraction and integration of human disease-related information from web-based genetic databases," Nucleic Acids Research, vol. 33, no. Web-Server-Issue, pp. 758–761, 2005. . Available: http://dx. doi. org/10. 1093/nar/gki435
  • H. Wang, H. Zheng, D. Simpson, and F. Azuaje, "Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data," Mar. 08 2006. . Available: http://www. biomedcentral. com/1471-2105/7/116
  • S. Rossi, D. Masotti, C. Nardini, E. Bonora, G. Romeo, E. Macii, Benini, and S. Volinia, "TOM: a web-based integrated approach for identification of candidate disease genes," Nucleic Acids Research,vol. 34, no. Web-Server-Issue, pp. 285–292, 2006. . Available: http://dx. doi. org/10. 1093/nar/gkl340
  • M. A. Harris and et al (!), "The gene ontology (GO) database and informatics resource," Nucleic Acids Res. , vol. 32, pp. D258–D261, Jan. 2004.
  • N. ria LoA pez Bigas and C. A, "Genome-wide identification of genes likely to be involved in human genetic disease. "
  • E. A. Adie, R. R. Adams, K. L. Evans, D. J. Porteous, and B. S. Pickard, "Speeding disease gene discovery by sequence based candidate prioritization," BMC Bioinformatics, vol. 6, p. 55, 2005. . Available: http://dx. doi. org/10. 1186/1471-2105-6-55
  • E. -W. A. Smith NGC, ": Human disease genes: patterns and predictions. "2003.
  • Y. Freund and L. Mason, "The alternating decision tree learning algorithm," in Proc. 16th International Conf. on Machine Learning. Morgan Kaufmann, San Francisco, CA, 1999, pp. 124–133.
  • C. Perez-Iratxeta, M. Wjst, P. Bork, and M. A. Andrade, "G2D: a tool for mining genes associated with disease. " . Available:http://www. pubmedcentral. gov/articlerender. fcgi?artid=1208881
  • C. Perez-Iratxeta, P. Bork, and M. A. Andrade-Navarro, "Update of the G2D tool for prioritization of gene candidates to inherited diseases, "Nucleic Acids Research, vol. 35, no. Web-Server-Issue, pp. 212–216,2007. . Available: http://dx. doi. org/10. 1093/nar/gkm223
  • M. Masseroli, "Management and analysis of genomic functional and phenotypic controlled annotations to support biomedical investigation and practice," IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 4, pp. 376–385, 2007. . Available:http://dx. doi. org/10. 1109/TITB. 2006. 884367
  • G. Casella and R. L. Berger, "Statistical inference, 2nd ed. belmont,. " Belmont, CA:Duxbury Press, 2002.
  • S. Peri, J. D. Navarro, R. Amanchy, T. Z. Kristiansen, C. K. Jonnalagadda, V. Surendranath, V. Niranjan, B. Muthusamy, T. K. B. Gandhi, M. Gronborg, N. Ibarrola, N. Deshpande, K. Shanker, H. N. Shivashankar, B. P. Rashmi, M. A. Ramya, Z. Zhao, K. N. , H. Steen, M. Tewari, S. Ghaffari, G. C. Blobe, C. V. Dang, J. G. N. Garcia, J. Pevsner, O. N. Jensen, P. Roepstorff, K. S. Deshpande, A. M. Chinnaiyan, A. Hamosh, A. Chakravarti, and A. Pandey, "Development of human protein reference database as an initial platform for approaching systems biology in humans," Jun. 21 2004. . Available:http://www. pubmedcentral. gov/articlerender. fcgi?artid=403728
  • Y. Chen, T. Jiang, and R. Jiang, "Uncover disease genes by maximizing information flow in the phenome-interactome network," Bioinformatics [ISMB/ECCB], vol. 27, no. 13, pp. 167–176, 2011. . Available:http://dx. doi. org/10. 1093/bioinformatics/btr213
  • ] S. Damian, H. Syed, B. Benoit, H. Richard, L. Darin, T. Gudmundur, and K. Arek, "Biomart – biological queries made easy," 2009