Skip to main content

Possibilistic Biclustering for Discovering Value-Coherent Overlapping \(\delta \)-Biclusters

  • Chapter
  • First Online:
Scalable Pattern Recognition Algorithms
  • 1411 Accesses

Abstract

The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Microarrays have been used to study different kinds of biological processes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ben-Dor A, Chor B, Karp R, Yakhini Z (2002) Discovering local structure in gene expression data: the order-preserving submatrix problem. In: Proceedings of the 6th international conference on computational biology, pp 49–57

    Google Scholar 

  2. Bezdek J (1980) A convergence theorem for the fuzzy ISODATA clustering algorithm. IEEE Trans Pattern Anal Mach Intell 2:1–8

    Article  MATH  Google Scholar 

  3. Bezdek J, Hathaway RJ, Sabin MJ, Tucker WT (1987) Convergence theory for fuzzy C-means: counterexamples and repairs. IEEE Trans Syst Man Cybern 17:873–877

    Article  MATH  Google Scholar 

  4. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithm. Plenum Press, New York

    Book  Google Scholar 

  5. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G (2004) GO:term finder open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinform 20(18):3710–3715

    Article  Google Scholar 

  6. Bryan K, Cunningham P, Bolshakova N (2005) Application of simulated annealing to the biclustering of gene expression data. In: Proceedings of the 18th IEEE symposium on computer-based medical systems, pp 383–388

    Google Scholar 

  7. Califano A, Stolovitzky G, Tu Y (2000) Analysis of gene expression microarrays for phenotype classifiation. In: Proceedings of the international conference on computational, molecular biology, pp 75–85

    Google Scholar 

  8. Cano C, Adarve L, Lopez J, Blanco A (2007) Possibilistic approach for biclustering microarray data. Comput Biol Med 37:1426–1436

    Article  Google Scholar 

  9. Chakraborty A, Maka H (2005) Biclustering of gene expression data using genetic algorithm. In: Proceedings of the IEEE symposium on computational intelligence in bioinformatics and computational biology, pp 1–8

    Google Scholar 

  10. Chen G, Sullivan PF, Kosoroka MR (2013) Biclustering with heterogeneous variance. Proc Nat Acad Sci U.S.A 110(30):12253–12258

    Google Scholar 

  11. Cheng Y, Church GM (2000) Biclustering of expression data. In: Proceedings of the 8th international conference on intelligent systems for, molecular biology, pp 93–103

    Google Scholar 

  12. Cho H, Dhillon I, Guan Y, Sra S (2004) Minimum sum-squared residue coclustering of gene expression data. In: Proceedings of the 4th SIAM international conference on data mining, pp 114–125

    Google Scholar 

  13. Das C, Maji P (2013) Possibilistic biclustering algorithm for discovering value-coherent overlapping \(\delta \)-Biclusters. Int J Mach Learn Cybern. doi:10.1007/s13042-013-0211-3

  14. Divina F, Aguilar-Ruiz JS (2006) Biclustering of expression data with evolutionary computation. IEEE Trans Knowl Data Eng 18(5):590–602

    Article  Google Scholar 

  15. Domany E (2003) Cluster analysis of gene expression data. J Stat Phys 110(3–6):1117–1139

    Article  MATH  Google Scholar 

  16. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Nat Acad Sci U.S.A 95(25):14863–14868

    Google Scholar 

  17. Eren K, Deveci M, Kucuktunc O, Catalyurek UV (2012) A comparative analysis of biclustering algorithms for gene expression data. Briefings in bioinformatics. doi:10.1093/bib/bbs032

  18. Fei X, Lu S, Pop HF, Liang LR (2007) GFBA: a biclustering algorithm for discovering value-coherent biclusters. Bioinformatics research and applications, pp 1–12

    Google Scholar 

  19. Getz G, Levine E, Domany E (2000) Coupled two-way clustering analysis of gene microarray data. Proc Nat Acad Sci U.S.A 97(22):12079–12084

    Google Scholar 

  20. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537

    Article  Google Scholar 

  21. Hartigan JA (1972) Direct clustering of a data matrix. J Am Stat Assoc 67(337):123–129

    Article  Google Scholar 

  22. Hartigan JA, Wong MA (1979) A K-means clustering algorithms. Appl Stat 28:100–108

    Article  MATH  Google Scholar 

  23. Herrero J, Valencia A, Dopazo J (2001) A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinform 17:126–136

    Article  Google Scholar 

  24. James G (1996) Modern engineering mathematics. Addison-Wesley, Reading

    MATH  Google Scholar 

  25. Jiang D, Tang C, Zhang A (2004) Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng 16(11):1370–1386

    Article  Google Scholar 

  26. Kaufmann L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis

    Google Scholar 

  27. Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98–110

    Article  Google Scholar 

  28. Lazzeroni L, Owen A (2000) Plaid models for gene expression data. Technical Report, Standford University

    Google Scholar 

  29. Lee M, Shen H, Huang JZ, Marron JS (2010) Biclustering via sparse singular value decomposition. Biometrics 66(4):1087–1095

    Article  MATH  MathSciNet  Google Scholar 

  30. Liu J, Wang W (2003) OP-cluster: clustering by tendency in high dimensional space. In: Proceedings of the 3rd IEEE international conference on data mining, pp 187–194

    Google Scholar 

  31. Madeira SC, Oliveira AL (2004) Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinform 1(1):24–45

    Article  Google Scholar 

  32. Maji P, Pal SK (2007) Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Trans Syst Man Cybern Part B Cybern 37(6):1529–1540

    Article  Google Scholar 

  33. Murali TM, Kasif S (2003) Extracting conserved gene expression motifs from gene expression data. In: Proceedings of the pacific symposium on biocomputing 8:77–88

    Google Scholar 

  34. Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer, Dordrecht

    Book  MATH  Google Scholar 

  35. Rodriguez-Baena DS, Perez-Pulido AJ, Aguilar-Ruiz JS (2011) A biclustering algorithm for extracting bit-patterns from binary data sets. Bioinform 27(19):2738–2745

    Google Scholar 

  36. Segal E, Taskar B, Gasch A, Friedman N, Koller D (2001) Rich probabilistic models for gene expression. Bioinform 17(S1):243–252

    Article  Google Scholar 

  37. Sheng Q, Moreau Y, Moor BD (2003) Biclustering microarray data by Gibbs sampling. Bioinform 19(S2):ii196–ii205

    Google Scholar 

  38. Sill M, Kaiser S, Benner A, Kopp-Schneider A (2011) Robust biclustering by sparse singular value decomposition incorporating stability selection. Bioinform 27(15):2089–2097

    Article  Google Scholar 

  39. Sutheeworapong S, Ota M, Ohta H, Kinoshita K (2012) A novel biclustering approach with iterative optimization to analyze gene expression data. Adv Appl Bioinform Chem 2012(5):23–59

    Google Scholar 

  40. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Nat Acad Sci U.S.A 96(6):2907–2912

    Article  Google Scholar 

  41. Tanay A, Sharan R, Shamir R (2002) Discovering statistically significant biclusters in gene expression data. Bioinform 18(S1):136–144

    Article  Google Scholar 

  42. Tang C, Zhang L, Zhang A, Ranmanathan M (2001) Interrelated two-way clustering: an unsupervised approach for gene expression data analysis. In: Proceedings of the 2nd IEEE international symposium on bioinformatics and bioengineering, pp 41–48

    Google Scholar 

  43. Tibshirani R, Hastie T, Eisen M, Ross D, Bostein D, Brown P (1999) Clustering methods for the analysis of DNA microarray data. Technical Report, Standford University

    Google Scholar 

  44. Tjhi WC, Chen L (2006) A partitioning based algorithm to fuzzy co-cluster documents and words. Pattern Recogn Lett 27:151–159

    Article  Google Scholar 

  45. Tjhi WC, Chen L (2007) Possibilistic fuzzy co-clustering of large document collections. Pattern Recogn 40:3452–3466

    Article  MATH  Google Scholar 

  46. Tjhi WC, Chen L (2008) A heuristic based fuzzy co-clustering algorithm for categorization of high dimensional data. Fuzzy Sets Syst 159:371–389

    Article  MATH  MathSciNet  Google Scholar 

  47. Tjhi WC, Chen L (2008) Dual fuzzy-possibilistic co-clustering for categorization of documents. IEEE Trans Fuzzy Syst 17(3):532–543

    Article  Google Scholar 

  48. Wang R, Miao D, Li G, Zhang H (2007) Rough overlapping biclustering of gene expression data. In: Proceedings of the 7th IEEE international conference on bioinformatics and bioengineering, pp 828–834

    Google Scholar 

  49. Wu CJ, Fu Y, Murali TM, Kasif S (2004) Gene expression module discovery using Gibbs sampling. Genome Inf 15(1):239–248

    Google Scholar 

  50. Yan H (2004) Convergence condition and efficient implementation of the fuzzy curve-tracing (FCT) algorithm. IEEE Trans Syst Man Cybern Part B Cybern 34(1):210–221

    Article  Google Scholar 

  51. Yang J, Wang W, Wang H, Yu PS (2003) Enhanced biclustering on expression data. In: Proceedings of the 3rd IEEE international conference on bioinformatics and bioengineering, pp 321–327

    Google Scholar 

  52. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pradipta Maji .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Maji, P., Paul, S. (2014). Possibilistic Biclustering for Discovering Value-Coherent Overlapping \(\delta \)-Biclusters. In: Scalable Pattern Recognition Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-05630-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05630-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05629-6

  • Online ISBN: 978-3-319-05630-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics