Advertisement

An artificial immune system-based algorithm for abnormal pattern in medical domain

  • L. Sharmila
  • U. Sakthi
Article
  • 57 Downloads

Abstract

In general, medical pattern is a collection of characters formed using the characters such as ‘a’, ‘c’, ‘g’ and ‘t’. The length of the pattern varies from one disease to another disease, and the pattern also seems to be different for different patients. Identifying an unusual pattern from the information design is troublesome a decade ago. In this paper, with a specific end goal to enhance the precision of the anomalous distinguishing pattern, an Artificial Immune System (AIS) is framed. In the present study, AIS is used to obtain the abnormal pattern by learning the characteristics of the entire data set. Due to the powerful and adaptive nature of AIS, detecting and identifying abnormal pattern is more accurate. This proposed idea is implemented in MATLAB software and experimented on DNA/RNA dataset and the performance is verified.

Keywords

Pattern matching Pattern recognition Artificial immune system Abnormal pattern detection Learning data 

References

  1. 1.
    Hulshizer R, Blalock EM (2007) Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data. BMC Bioinform 8:240CrossRefGoogle Scholar
  2. 2.
    Perelman S, Mazzella MA, Muschietti J, Zhu T, Casal JJ (2003) Finding unexpected patterns in microarray data. Plant Physiol 133(4):1717–1725CrossRefGoogle Scholar
  3. 3.
    Valafar F (2002) Pattern recognition techniques in microarray data analysis: a survey. Ann NY Acad Sci 980:41–64CrossRefGoogle Scholar
  4. 4.
    Stekel D (2006) Microarray bioinformatics. Cambridge University Press, CambridgeGoogle Scholar
  5. 5.
    Ben-Dor A, Shamir R, Yakhini Z (1999) Clustering gene expression patterns. J Comput Biol 6(3–4):281–297CrossRefGoogle Scholar
  6. 6.
    Eisen M, Spellman P, Brown P, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci 95:863–868CrossRefGoogle Scholar
  7. 7.
    Jiang D, Tang C, Zhang A (2003) Cluster analysis for gene expression data: a survey. www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/survey (2003)
  8. 8.
    Bohern BF, Hanley EN Jr (1995) Extracting knowledge from large medical databases: an automated approach. Comput Biomed Res 28:191–210CrossRefGoogle Scholar
  9. 9.
    Altschul SF, Miller W (1990) Basic local alignment search tool. J Mol Biol 215:403–410CrossRefGoogle Scholar
  10. 10.
    Iyer VR (1999) The transcriptional program in the response of human fibroblasts to serum”. Science 283:83–87CrossRefGoogle Scholar
  11. 11.
    Eisen MB et al (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95(25):14863–14868CrossRefGoogle Scholar
  12. 12.
    Tavazoie S (1999) Systematic determination of genetic network architecture. Nature Genet 22:281–285CrossRefGoogle Scholar
  13. 13.
    Tamayo P (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96(6):2907–2912CrossRefGoogle Scholar
  14. 14.
    Alon U (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide array. Proc Natl Acad Sci USA 96(12):6745–6750CrossRefGoogle Scholar
  15. 15.
    Ben-Dor A, Shamir R, Yakhini Z (1999) Clustering gene expression patterns. J Comput Biol 6(3/4):281–297CrossRefGoogle Scholar
  16. 16.
    De Smet Frank (2002) Adaptive quality-based clustering of gene expression profiles. Bioinformatics 18:735–746CrossRefGoogle Scholar
  17. 17.
    Cavallaro S, D’Agata V, Manickam P, Dufour F, Alkon DL (2002) Memory specific temporal profiles of gene expression in the hippocampus. Proc Natl Acad Sci USA 99(25):16279–16284CrossRefGoogle Scholar
  18. 18.
    Pavlidis P, Noble WS (2001) Analysis of strain and regional variation in gene expression in mouse brain. Genome Biol 2(10)Google Scholar
  19. 19.
    Reid R, Dix DJ, Miller D, Krawetz SA (2001) Recovering filter-based microarray data for pathways analysis using a multipoint alignment strategy. Biotechniques 30(4):762–766Google Scholar
  20. 20.
    Genter MB, Van Veldhoven PP, Jegga AG, Sakthivel B, Kong S, Stanley K, Witte DP, Ebert CL, Aronow BJ (2003) Microarray-based discovery of highly expressed olfactory mucosal genes: potential roles in the various functions of the olfactory system. Physiol Genomics 16(1):67–81CrossRefGoogle Scholar
  21. 21.
    Hutton JJ, Jegga AG, Kong S, Gupta A, Ebert C, Williams S, Katz JD, Aronow BJ (2004) Microarray and comparative genomics-based identification of genes and gene regulatory regions of the mouse immune system. BMC Genom 5(1):82CrossRefGoogle Scholar
  22. 22.
    Li H, Wood CL, Liu Y, Getchell TV, Getchell ML, Stromberg AJ (2006) Identification of gene expression patterns using planned linear contrasts. BMC Bioinform 7:245CrossRefGoogle Scholar
  23. 23.
    Liu H, Tarima S, Borders AS, Getchell TV, Getchell ML, Stromberg AJ (2005) Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments. BMC Bioinform 6:106CrossRefGoogle Scholar
  24. 24.
    Balasubramaniyan R, Hullermeier E, Weskamp N, Kamper J (2005) Clustering of gene expression data using a local shape-based similarity measure. Bioinformatics 21(7):1069–1077CrossRefGoogle Scholar
  25. 25.
    Conesa A, Nueda MJ, Ferrer A, Talon M (2006) maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 22(9):1096–1102CrossRefGoogle Scholar
  26. 26.
    Eckel JE, Gennings C, Chinchilli VM, Burgoon LD, Zacharewski TR (2004) Empirical bayes gene screening tool for time-course or dose–response microarray data. J Biopharm Stat 14(3):647–670MathSciNetCrossRefGoogle Scholar
  27. 27.
    de Castro LN, Timmis J (2002) Artificial immune systems: a novel paradigm to pattern recognition, SOCO-2002, University of Paisley, UK, pp 67-84Google Scholar
  28. 28.
    Sharmila L, Sakthi U, Sagadevan Suresh (2017) A support vector machine based dynamic clustering and classification on gene expression data. Int J of Chemtech Res 10(4):442–447Google Scholar
  29. 29.
    Somogyi R, Wen X, Ma W, Barker JL (1995) Developmental Kinetics of GAD family mRNAs parallel neurogenesis in the rat spinal cord. J Neuro Sci 15(4):2575, 2591Google Scholar
  30. 30.
    Wen X, Fuhrman S, Michales GS, Sarr DB, Smith S (1999) Large-scale temporal gene expression mapping of central nervous system development. PNAS 95(1):334, 339Google Scholar
  31. 31.
    Eisen MB, Spellman PT, Prown PO (1999) Cluster analysis and display of genome-wide expression pattern. PNAS 95:14863, 14868Google Scholar
  32. 32.
    Spellman PT, Sherlock G et al (1998) Comprehensive identification of cell cycle-regulated genes of he yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9:3273, 3297CrossRefGoogle Scholar
  33. 33.
    Tmaayo P, Slonim D, Medirov J (1999) Interpreting patterns of gene expression with SOM: Methods Appl Hematopoietic Differ. PNAS 96:2907, 2912Google Scholar
  34. 34.
    Alon U, Barkai N, Notterman DA et al (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. PNAS 96:6745–6750CrossRefGoogle Scholar
  35. 35.
    Gordon AD (1999) Classification, 2nd edn. Chapman and Hall/CRC, Boca RatonzbMATHGoogle Scholar
  36. 36.
    Bock H (1996) Probabilistic models in cluster analysis. Comput Stat Data An. 23:5–28CrossRefzbMATHGoogle Scholar
  37. 37.
    Golub TR, Slonim DK, Tamayo P et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537CrossRefGoogle Scholar
  38. 38.
    Alizadeh AA, Eisen MB, Davis RE et al (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769):503–511CrossRefGoogle Scholar
  39. 39.
    Bittner M, Meltzer P, Chen Y et al (2000) Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406(6795):536–540CrossRefGoogle Scholar
  40. 40.
    Nielsen TO, West RB, Linn SC et al (2002) Molecular characterization of soft tissue tumours: a gene expression study. Lancet 359(9314):1301–1307CrossRefGoogle Scholar
  41. 41.
    Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99(10):6567–6572CrossRefGoogle Scholar
  42. 42.
    Parmigiani G, Garrett ES, Anbazhagan R, Gabrielson E (2002) A statistical framework for expression-based molecular classification in cancer. J Roy Statist SocSer B. 64(4):717–736MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    Sharmila L, Sakthi U (2016) Analysis on various search algorithms. Global J Pure Appl Math 12(2):1397–1402Google Scholar
  44. 44.
    Sharmila L, Sakthi U (2018) Chronological pattern exploration algorithm for gene expression data clustering and classification. Wirel Pers Commun 98(1). ISSN 0929-6212Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Faculty of CSESathyabama Institute of Science and TechnologyChennaiIndia
  2. 2.CSEAlpha College of EngineeringChennaiIndia
  3. 3.Department of Computer Science and EngineeringSt. Joseph’s Institute of TechnologyChennaiIndia

Personalised recommendations