Current Science

Open Access Open Access  Restricted Access Subscription Access

Soft Fuzzy Model for Mining Amino Acid Associations in Peptide Sequences of Mycobacterium tuberculosis Complex


Affiliations
1 Department of Computer Application, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal 462 003, India
2 Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal 462 003, India
 

Analysis of biological data plays an important role in medical and bioinformatics industry. However, uncertainty in this biological information is the most unavoidable challenge of this era. The existing algorithms for association rule mining are inadequate to address the issues of uncertainty in the molecular data. Variation in the length of the sequences leads to variation in the degree of relationships among amino acids. Ignorance of the parameters leads to uncertainty due to the dependencies of the objects and their patterns on the parameters. The degree of relationships among various amino acids present in the molecular sequences also depends on the parameters like length ranges and species, etc. In this article, a soft fuzzy set approach has been proposed for mining fuzzy amino acid associations in peptide sequences of Mycobacterium tuberculosis complex (MTBC). The approach is employed to incorporate the degree of relationships among amino acids present in the peptide sequences. The soft sets are employed to model relationships of amino acids with the parameters like length range, species etc. The amino acid associations and their relationships with various parameters in the peptide sequences of MTBC obtained in the present study will be of great use in developing signatures that will provide better insights into the structures, functions and interactions of proteins.

Keywords

Association Rule, Complex, Data Mining, Fuzzy and Soft Sets, Mycobacterium tuberculosis.
User
Notifications
Font Size

  • Agrawal, R., Imielinski, T. and Swami, A. N., Mining association rules between sets of items in large databases. ACM SIGMOD Record, 1993, 22(2), 207–216.

  • Agrawal, R. and Srikant, R., Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Databases, VLDB, 12 September 1994, vol. 1215, pp. 407–419.

  • Patel, R., Swami, D. K. and Pardasani, K. R., Lattice based algorithm for incremental mining of association rules. Int. J. Theor. Appl. Comput. Sci., 2006, 1(1), 119–128.

  • Pandey, A. and Pardasani, K., Rough set model for discovering multidimensional association rules. IJCSNS Int. J. Comput. Sci. Network Security, 2009, 9(6), 159–164.

  • Panday, A. and Pardasani, K. R., PPCI algorithm for mining temporal association rules in large database. J. Inf. Knowledge Manage., 2009, 8(04), 345–352.

  • Khare, N., Adlakha, N. and Pardasani, K. R., Karnaugh map model for mining association rules in large databases, IJCNS Int. J. Comput. Network Security, 2009, 1(1), 16–21.

  • Kocatas, A., Gursoy, A. and Atalay, R., Application of data mining techniques to protein–protein interaction prediction. In Computer and Information Sciences-ISCIS, Springer, Berlin, Heidelberg 2003, pp. 316–323.

  • Rodríguez, A., Carazo, J. M. and Trelles, O., Mining association rules from biological databases. J. Am. Soc. Inf. Sci. Technol., 2005, 56(5), 493–504.

  • Oyama, T., Kitano, K., Satou, K. and Ito, T., Extraction of knowledge on protein–protein interaction by association rule discovery. Bioinformatics, 2002, 18(5), 705–714.

  • Kuo, H. C., Ong, P. L., Lin, J. C. and Huang, J. P., Discovering amino acid patterns on binding sites in protein complexes. Bioinformation, 2011, 6(1), p. 10.

  • Intan, R., An algorithm for generating single dimensional fuzzy association rule mining. J. Informat., 2006, 7(1), p. 61.

  • Khare, N., Adlakha, N. and Pardasani, K. R., An algorithm for mining multidimensional fuzzy association rules. Int. J. Comput. Sci. Inform. Security, 2009, 5(1), 72–76.

  • Khare, N., Adlakha, N. and Pardasani, K. R., A fuzzy based model for mining conditional hybrid dimensional association rules. Int. J. Data Min. Knowledge Eng., 2010, 2(5), 69–76.

  • Gautam, P. and Pardasani, K. R., A novel approach for discovery of multilevel fuzzy association rules. J. Comput., 2010, 2(3), 56–64.

  • Gupta, N., Mangal, N., Tiwari, K. and Mitra, P., Mining quantitative association rules in protein sequences. In Data Mining, Springer, Berlin, Hidelberg, 2006, vol. 3755, pp. 273–281.

  • Francisco, J. L., Armando, B., Fernando, G., Carlos, C. and Antonio, M., FUZZY association rules for biological data analysis: a case study on yeast. BMC Bioinforma., 2008, 9(1), 107.

  • Kumari, T. and Pardasani, K. R., Mining fuzzy associations among amino acids of class A GPCRs. Online J. Bioinformat., 2012, 13(2), 202–213.

  • Kumari, T. and Pardasani, K. R., Mining amino acid association patterns in class B GPCRs. Int. J. Bioinformat. Res. Appl., 2015, 11(3), 219–232.

  • Shankar, A. and Pardasani, K. R., Mining fuzzy amino acid association patterns in various orders of class Alphaproteobacteria. J. Med. Imag. Health Informat., 2013, 3(3), 380–387.

  • Molodtsov, D., Soft set theory – first results. Comput. Math. Appl., 1999, 37(4), 19–31.

  • Herawan, T. and Mustafa, M. D., A soft set approach for association rules mining. Knowledge-Based Syst., 2011, 24(1), 186–195.

  • World Health Organization, report 2013 – Global tuberculosis report.

  • Cole, T. S., Comparative and functional genomics of the Mycobacterium tuberculosis complex. Microbiology, 2002, 148(10), 2919–2928.

  • Saravanan, M. K. and Selvaraj, S., Search for identical octapeptides in unrelated proteins: Structural plasticity revisited. Peptide Sci., 2012, 98(1), 11–26.

  • Uthayakumar, M., Patra, S., Nagarajan, R. and Sekar, K., Sequence–structure similarity: do sequentially identical peptide fragments have similar three-dimensional structures? Curr. Bioinformat., 2012, 7(2), 111–115.

  • Brosch, R. et al., A new evolutionary scenario for the Mycobacterium tuberculosis complex. Proc. Natl. Acad. Sci. USA, 2002, 99(3), 684–3689.

  • Shabbeer, A., Cowan, L. S., Ozcaglar, C., Rastogi, N., Vandenberg, S. L., Yener, B. and Bennett, K. P., TB-lineage: an online tool for classification and analysis of strains of Mycobacterium tuberculosis complex. Infect. Genet. Evol., 2012, 12(4), 789–797.

  • http://www.ncbi.nlm.nih.gov/


Abstract Views: 406

PDF Views: 127




  • Soft Fuzzy Model for Mining Amino Acid Associations in Peptide Sequences of Mycobacterium tuberculosis Complex

Abstract Views: 406  |  PDF Views: 127

Authors

Amita Jain
Department of Computer Application, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal 462 003, India
Kamal Raj Pardasani
Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal 462 003, India

Abstract


Analysis of biological data plays an important role in medical and bioinformatics industry. However, uncertainty in this biological information is the most unavoidable challenge of this era. The existing algorithms for association rule mining are inadequate to address the issues of uncertainty in the molecular data. Variation in the length of the sequences leads to variation in the degree of relationships among amino acids. Ignorance of the parameters leads to uncertainty due to the dependencies of the objects and their patterns on the parameters. The degree of relationships among various amino acids present in the molecular sequences also depends on the parameters like length ranges and species, etc. In this article, a soft fuzzy set approach has been proposed for mining fuzzy amino acid associations in peptide sequences of Mycobacterium tuberculosis complex (MTBC). The approach is employed to incorporate the degree of relationships among amino acids present in the peptide sequences. The soft sets are employed to model relationships of amino acids with the parameters like length range, species etc. The amino acid associations and their relationships with various parameters in the peptide sequences of MTBC obtained in the present study will be of great use in developing signatures that will provide better insights into the structures, functions and interactions of proteins.

Keywords


Association Rule, Complex, Data Mining, Fuzzy and Soft Sets, Mycobacterium tuberculosis.

References





DOI: https://doi.org/10.18520/cs%2Fv110%2Fi4%2F603-618