Skip to main content
SpringerLink
Log in

Recent Advances of Data Biclustering with Application in Computational Neuroscience

  • Chapter
  • First Online:
Computational Neuroscience

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 38))

Abstract

Clustering and biclustering are important techniques arising in data mining. Different from clustering, biclustering simultaneously groups the objects and features according their expression levels. In this review, the backgrounds, motivation, data input, objective tasks, and history of data biclustering are carefully studied. The bicluster types and biclustering structures of data matrix are defined mathematically. Most recent algorithms, including OREO, nsNMF, BBC, cMonkey, etc., are reviewed with formal mathematical models. Additionally, a match score between biclusters is defined to compare algorithms. The application of biclustering in computational neuroscience is also reviewed in this chapter.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Angiulli, F., Cesario, E., Pizzuti, C. Random walk biclustering for microarray data. Inf Sci: Int J 178(6), 1479–1497 (2008)

    MATH  Google Scholar 

  2. Barkow, S., et al. BicAT: A biclustering analysis toolbox. Bioinformatics 22, 1282–1283 (2006)

    Article  Google Scholar 

  3. Ben-Dor, A., Chor, B., Karp, R., Yakhini, Z. Discovering local structure in gene expression data: The order-preserving submatrix problem. J Comput Biol 10, 373–384 (2003)

    Article  Google Scholar 

  4. Busygin, S., Prokopyev, O.A., Pardalos, P.M. Feature selection for consistent biclustering via fractional 0–1 programming. J Comb Optim 10/1, 7–21 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  5. Busygin, S., Prokopyev, O.A., Pardalos, P.M. Biclustering in datamining. Comput Oper Res 35, 2964–2987 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  6. Busygin, S., Boyko, N., Pardalos, P., Bewernitz, M., Ghacibehc, G. Biclustering EEG data from epileptic patients treated with vagus nerve stimulation. AIP Conference Proceedings of the Data Mining, Systems Analysis and Optimization in Biomedicine, 220–231 (2007)

    Google Scholar 

  7. Califano, A., Stolovitzky, G., Tu, Y. Analysis of gene expression microarays for phenotype classification. Proceedings of International Conference on Computational Molecular Biology, 75–85 (2000)

    Google Scholar 

  8. Carmona-Saez, P., Pascual-Marqui, R.D., Tirado, F., Carazo, J.M., Pascual-Montano, A. Biclustering of gene expression data by non-smooth non-negative matrix factorization. BMC Bioinformatics 7, 78 (2006)

    Article  Google Scholar 

  9. Chaovalitwongse, W.A., Butenko, S., Pardalos, P.M. Clustering Challenges in Biological Networks, World Scientific Publishing, Singapore (2008)

    Google Scholar 

  10. Cheng, K.O., et al. Bivisu: Software tool for bicluster detection and visualization. Bioinformatics 23, 2342–2344 (2007)

    Article  Google Scholar 

  11. Cheng, Y., Church, G.M. Biclustering of expression data. Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, 93–103 (2000)

    Google Scholar 

  12. Cho, H., Dhillon, I.S. Coclustering of human cancer microarrays using minimum sum-squared residue coclustering. IEEE/ACM Trans Comput Biol Bioinform 5(3), 385–400 (2008)

    Article  Google Scholar 

  13. Chung, F.R.K. Spectral graph theory. Conference Board of the Mathematical Sciences, Number 92, American Mathematical Society (1997)

    Google Scholar 

  14. CPLEX: ILOG CPLEX 9.0 Users Manual (2005)

    Google Scholar 

  15. Data Clustering. http://en.wikipedia.org/wiki/Data clustering, access at Dec. 8 (2008)

  16. Data Transformation Steps. http://www.dmg.org/v2–0/Transformations.html, access at Dec. 8 (2008)

    Google Scholar 

  17. Dhillon, I.S. Co-clustering documents and words using bipartite spectral graph partitioning. Proceedings of the 7th ACM SIGKDD International Conference on Knowledging Discovery and Data Mining (KDD), 26–29 (2001)

    Google Scholar 

  18. Dhillon, I.S., Mallela, S., Modha, D.S. Information theoretic co-clustering. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 89–98 (2003)

    Google Scholar 

  19. DiMaggio, P.A., McAllister, S.R., Floudas, C.A., Feng, X.J., Rabinowitz, J.D., Rabitz, H.A. Biclustering via optimal re-ordering of data matrices in systems biology: Rigorous methods and comparative studies. BMC Bioinformatics 9, 458 (2008)

    Article  Google Scholar 

  20. Engel, J. Jr. Seizures and Epilepsy. F. A. Davis Co., Philadelphia, PA (1989)

    Google Scholar 

  21. Engel, J. Jr., Pedley, T.A. Epilepsy: A Comprehensive Textbook. Lippincott-Raven, Philadelphia, PA (1997)

    Google Scholar 

  22. Fan, N., Chinchuluun, A., Pardalos, P.M. Integer programming of biclustering based on graph models, In: Chinchuluun, A., Pardalos, P.M., Enkhbat, R. and Tseveendorj, I. (eds.) Optimization and Optimal Control: Theory and Applications, Springer (2009)

    Google Scholar 

  23. Fan, N., Pardalos, P.M. Linear and quadratic programming approaches for the general graph partitioning problem, J Global Optim, DOI 10.1007/s10898-009-9520-1, (2010)

    Google Scholar 

  24. Fisher, R.S., Krauss, G.L., Ramsay, E., Laxer, K., Gates, J. Assessment of vagus nerve stimulation for epilepsy: Report of the therapeutics and technology assessment subcommittee of the American academy of neurology. Neurology 49, 293–297 (1997)

    Google Scholar 

  25. Fisher, R.S., Theodore W.H. Brain stimulation for epilepsy. Lancet Neurol 3(2), 111–118 (2004)

    Article  Google Scholar 

  26. Gu, J., Liu, J.S. Bayesian biclustering of gene expression data. BMC Genom 9(Suppl 1), S4 (2008)

    Google Scholar 

  27. Hagen, L., Kahng, A.B. New spectral methods for ratio cut partitioning and clustering. IEEE Trans Computer-Aided Design 11(9), 1074–1085 (1992)

    Article  Google Scholar 

  28. Hartigan, J.A. Direct clustering of a data matrix. J Am Stat Assoc 67, 123–129 (1972)

    Article  Google Scholar 

  29. Iasemidis, L.D., Principe, J.C., Sackellares, J.C. Measurement and quantification of spatiotemporal dynamics of human epilepic seizures. In: Akay, M. (ed.) Nonlinear Signal Processing in Medicine, IEEE Press (1999)

    Google Scholar 

  30. Ihmels, J., Friedlander, G., Bergmann, S., Sarig, O., Ziv, Y., Barkai, N. Revealing modular organization in the yeast transcriptional network. Nat Genet 31(4), 370–377 (2002)

    Google Scholar 

  31. Jain, A.K., Murty, M.N., Flynn, P.J. Data clustering: A review. ACM Comput Survey 31(3), 264–323 (1999)

    Article  Google Scholar 

  32. Kaiser, S., Leisch, F. A toolbox for bicluster analysis in r. Tech. Rep. 028, Ludwing-Maximilians-Universitat Mnchen (2008)

    Google Scholar 

  33. Kluger, Y., Basri, R., Chang, J.T., Gerstein, M. Spectral biclustering of microarray cancer data: Co-clustering genes and conditions. Genome Res 13, 703–716 (2003)

    Article  Google Scholar 

  34. Lazzeroni, L., Owen, A. Plaid models for gene expression data. Stat Sinica 12, 61C86 (2002)

    Google Scholar 

  35. Lee, D.D., Seung, H.S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)

    Article  Google Scholar 

  36. Liu, J., Wang, W. OP-cluster: Clustering by tendency in high dimensional space. Proceedings of the Third IEEE International Conference on Data Mining, 187–194 (2003)

    Google Scholar 

  37. Madeira, S.C., Oliveira, A.L. Biclustering algorithms for biological data analysis: A survey. IEEE Trans Comput Biol Bioinform 1(1), 24–45 (2004)

    Article  Google Scholar 

  38. Madeira, S.C., Oliveira, A.L. A linear time biclustering algorithm for time series gene expression data. Lect Notes Comput Sci 3692, 39–52, (2005)

    Article  MathSciNet  Google Scholar 

  39. Murali, T.M., Kasif, S. Extracting conserved gene expression motifs from gene expression data. Pacific Symp Biocomput 8, 77–88 (2003)

    Google Scholar 

  40. Pardalos, P.M., Busygin, S., Prokopyev, O.A. On biclustering with feature selection for microarray data sets. In: Mondaini, R. (ed.) BIOMAT 2005łinternational Symposium on Mathematical and Computational Biology, pp. 367–378. World Scientific, Singapore (2006)

    Google Scholar 

  41. Pardalos, P.M., Chaovalitwongse, W., Iasemidis, L.D., Sackellares, J.C., Shiau, D.-S., Carney, P.R., Prokopyev, O.A., Yatsenko, V.A. Seizure warning algorithm based on optimization and nonlinear dynamics. Math Prog 101(2), 365–385 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  42. Pardalos, P.M., Chaovalitwongse, W., Prokopyev, O. Electroencephalogram (EEG) time series classification: Application in epilepsy. Ann Oper Res (2006)

    Google Scholar 

  43. Pascual-Montano, A., Carazo, J.M., Kochi, K., Lehmann, D., Pascual-Marqui, R.D. Non-smooth Non-negative matrix factorization (nsNMF). IEEE Trans Pattern Anal Mach Intell 28, 403–415 (2006)

    Article  Google Scholar 

  44. Prelic, A., Bleuler, S., Zimmermann, P., Wille, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E. A systematic comparison and evaluation of biclusteringmethods for gene expression data. Bioinformatics 22(9), 1122–1129, (2006)

    Article  Google Scholar 

  45. Rege, M., Dong, M., Fotouhi, F. Bipartite isoperimetric graph partitioning for data co-clustering. Data Min Know Disc 16, 276–312 (2008)

    Article  MathSciNet  Google Scholar 

  46. Reiss, D.J., Baliga, N.S., Bonneau, R. Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks. BMC Bioinformatics 7, 280 (2006)

    Article  Google Scholar 

  47. Richards, A.L., Holmans, P.A., O'Donovan, M.C., Owen, M.J., Jones, L. A comparison of four clustering methods for brain expression microarray data. BMC Bioinformatics 9, 490 (2008)

    Article  Google Scholar 

  48. Santamaria, R., Theron, R., Quintales, L. BicOverlapper: A tool for bicluster visualization Rodrigo. Bioinformatics 24, 1212–1213 (2008)

    Article  Google Scholar 

  49. Schachter, S.C., Wheless, J.W. (eds.) Vagus nerve stimulation therapy 5 years after approval: A comprehensive update. Neurology S4, 59 (2002)

    Google Scholar 

  50. Sheng, Q., Moreau, Y., De Moor, B. Biclustering microarray data by Gibbs sampling. Bioinformatics 19, 196–205 (2003)

    Article  Google Scholar 

  51. Shi, J., Malik, J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell, 22(8), 888–905 (2000)

    Article  Google Scholar 

  52. Supper, J., Strauch, M., Wanke, D., Harter, K., Zell, A. EDISA: Extracting biclusters from multiple time-series of gene expression profiles. BMC Bioinformatics 8, 334 (2007)

    Article  Google Scholar 

  53. Tanay, A., Sharan, R., Kupiec, M., Shamir, R. Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc Natl Acad Sci USA 101, 2981–2986 (2004)

    Article  Google Scholar 

  54. Tanay, A., Sharan, R., Shamir, R. Discovering statistically significant bilcusters in gene expression data. Bioinformatics 18, S136–S144 (2002)

    Google Scholar 

  55. Tanay, A., Sharan, R., Shamir, R. Biclustering algorithms: A survey. In: Aluru, S. (ed.) Handbook of Computational Molecular Biology. Chapman Hall, London (2005)

    Google Scholar 

  56. Uthman, B.M., Wilder, B.J., Penry, J.K., Dean, C., Ramsay, R.E., Reid, S.A., Hammond, E.J., Tarver, W.B., Wernicke, J.F. Treatment of epilepsy by stimulation of the vagus nerve. Neurology 43, 1338–1345 (1993)

    Article  Google Scholar 

  57. Xu, R., Wunsch, D. II. Survey of clustering algorithms. IEEE Trans Neural Netw 16(3), 645–678 (2005)

    Article  Google Scholar 

  58. Yang, J., Wang, W., Wang, H., Yu, P. δ -Clusters: Capturing subspace correlation in a large data set. Proceedings of the 18th IEEE International Conference on Data Engineering, 517–528 (2002)

    Google Scholar 

  59. Yang, J., Wang, W., Wang, H., Yu, P. Enhanced biclustering on expression data. Proceedings of the Third IEEE Conference on Bioinformatics and Bioengineering, 321–327 (2003)

    Google Scholar 

  60. Zha, H., He, X., Ding, C., Simon, H., Gu, M. Bipartite graph partitioning and data clustering. Proceedings of the Tenth International Conference on Information and Knowledge Management, 25–32 (2001)

    Google Scholar 

  61. Zhao, H., Liew, A.W.-C., Xie, X., Yan, H. A new geometric biclustering based on the Hough transform for analysis of large-scale microarray data. J Theor Biol 251, 264–274 (2008)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Neng Fan, Nikita Boyko & Panos M. Pardalos

Authors
  1. Neng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nikita Boyko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Panos M. Pardalos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neng Fan .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems, Engineering, Rutgers State University of New Jersey, Frelinghuysen Rd. 96, Piscataway, 08854, New Jersey, USA

    Wanpracha Chaovalitwongse

  2. Dept. Industrial & Systems, Engineering, University of Florida, Weil Hall 303, Gainesville, 32611-6595, Florida, USA

    Panos M. Pardalos

  3. , Dept. of Industrial & Systems Eng., University of Florida, Weil Hall 303, Gainesville, 32611, Florida, USA

    Petros Xanthopoulos

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Fan, N., Boyko, N., Pardalos, P.M. (2010). Recent Advances of Data Biclustering with Application in Computational Neuroscience. In: Chaovalitwongse, W., Pardalos, P., Xanthopoulos, P. (eds) Computational Neuroscience. Springer Optimization and Its Applications(), vol 38. Springer, New York, NY. https://doi.org/10.1007/978-0-387-88630-5_6

Download citation

Publish with us

Policies and ethics

Search

Navigation