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Identification of Deleterious SNPs in TACR1 Gene Using Genetic Algorithm

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSFOMEBI)

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

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  • DOI: 10.1007/978-981-287-338-5_7
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References

  1. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (s.l)

    Google Scholar 

  2. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. Morgan Kaufmann, Los Altos (Third s.l)

    Google Scholar 

  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. 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):91

    Google Scholar 

  5. Permann MR (2007) Genetic algorithms for agent-based infrastructure interdependency modeling and analysis. INL/CON-07-12317, SpringSim

    Google Scholar 

  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. Ng CP, Henikoff S (2001) Predicting deleterious amino acid substitutions. Genome Res 11:863–874

    CrossRef  Google Scholar 

  8. Ng CP, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814

    CrossRef  Google Scholar 

  9. http://www.ncbi.nlm.nih.gov/SNP/

  10. Carvalho DR, Freitas AA An immunological algorithm for discovering small-disjunct rules in data mining

    Google Scholar 

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Correspondence to Dharmaiah Devarapalli .

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Devarapalli, D., Anusha, C., Srikanth, P. (2015). Identification of Deleterious SNPs in TACR1 Gene Using Genetic Algorithm. In: Muppalaneni, N., Gunjan, V. (eds) Computational Intelligence Techniques for Comparative Genomics. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-287-338-5_7

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  • DOI: https://doi.org/10.1007/978-981-287-338-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-337-8

  • Online ISBN: 978-981-287-338-5

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