Advertisement

Identification of Deleterious SNPs in TACR1 Gene Using Genetic Algorithm

  • Dharmaiah DevarapalliEmail author
  • Ch. Anusha
  • Panigrahi Srikanth
Chapter
  • 910 Downloads
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

Bioinformatics is a specific research and development area. The purpose of bioinformatics mainly deals with data mining and the relationships and patterns in large databases to provide useful information analysis and diagnosis. Single nucleotide polymorphisms (SNP) are one of the major causes of genetic diseases. Identification of disease-causing SNPs can identify better disease diagnosis. Hence, the present study aims at the identification of deleterious SNPs in TACR1 gene. Developing an algorithm plays a vital role in computational intelligence techniques. In this paper, a genetic algorithm (GA) approach is to develop rules and it is presented. The importance of the accuracy, sensitivity, specificity, and comprehensibility of the rules is simplified for the implementation of a GA. The outline of encoding and genetic operators and fitness function of GA are discussed. GA is using to identify deleterious or damaged SNPs.

Keywords

SNPs in TACR1 Computational intelligence technique Fitness function Genetic algorithm 

References

  1. 1.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (s.l)Google Scholar
  2. 2.
    Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. Morgan Kaufmann, Los Altos (Third s.l)Google Scholar
  3. 3.
    Al-Maqaleh BM, Shahbazkia H (2012) A genetic algorithm for discovering classification rules in data mining. Int J Comput Appl 41(18):40–44 (0975–8887)Google Scholar
  4. 4.
    Pradhan MA, Bamnote GR, Tribhuvan V, Jadhav K, Chabukswar V, Dhobale V (2012) A genetic programming approach for detection of diabetes. Int J Comput Eng Res (ijceronline.com) 2(6):91Google Scholar
  5. 5.
    Permann MR (2007) Genetic algorithms for agent-based infrastructure interdependency modeling and analysis. INL/CON-07-12317, SpringSimGoogle Scholar
  6. 6.
    Datar P, Srivastava S, Coutinho E, Govil G (2004) Substance P: structure, function, and therapeutics. Curr Top Med Chem 4(1):75.103. doi: 10.2174/1568026043451636 (PMID14754378)
  7. 7.
    Ng CP, Henikoff S (2001) Predicting deleterious amino acid substitutions. Genome Res 11:863–874CrossRefGoogle Scholar
  8. 8.
    Ng CP, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Carvalho DR, Freitas AA An immunological algorithm for discovering small-disjunct rules in data miningGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Dharmaiah Devarapalli
    • 1
    Email author
  • Ch. Anusha
    • 1
  • Panigrahi Srikanth
    • 2
  1. 1.Department of Computer Science and EngineeringVignan’s Institute of Information TechnologyDuvvada, ViskhapatnamIndia
  2. 2.Department of Computer Science and Engineering (Software Engineering)Vignan’s Institute of Information TechnologyDuvvada, ViskhapatnamIndia

Personalised recommendations