Skip to main content
SpringerLink
Log in

A PATTERN-MINING METHOD FOR HIGH-THROUGHPUT LAB-ON-A-CHIP DATA ANALYSIS

  • Chapter
Design Automation Methods and Tools for Microfluidics-Based Biochips

  • 1089 Accesses

Abstract

Biochips are emerging as a useful tool for high-throughput acquisition of biological data and continue to grow in information quality and in discovering new applications. Recent advances include CMOS-based integrated biosensor arrays for deoxyribonucleic acid (DNA) expression analysis [35, 17], and active research is ongoing for the miniaturization and integration of protein microarrays [36, 19, 33], tissue microarrays (TMAs) [37, 8], and fluorescence-based multiplexed cytokine immunoassays [41].

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

REFERENCES

  1. A. V. Aho, J. E. Hopcroft, and J. D. Ullman. Data Structures and Algorithms. Addison- Wesley, Reading, Massachusetts, 1983.

    MATH  Google Scholar 

  2. R. B. Altman and S. Raychaudhuri. Whole-genome expression analysis: challenges beyond clustering. Current Opinion in Structural Biology, 11:340–347, 2001.

    Article  Google Scholar 

  3. A. Ben-Dor, B. Chor, R. Karp, and Z. Yakhini. Discovering local structure in gene expression data: The order-preserving submatrix problem. Journal of Computational Biology, 10(3–4):373–384, 2003.

    Article  Google Scholar 

  4. K. S. Brace, R. L. Rudell, and R. E. Bryant. Efficient implementation of a BDD package. In Proceedings of the 27th Design Automation Conference, pages 40–45, 1990.

    Google Scholar 

  5. R. E. Bryant. Graph-based algorithms for Boolean function manipulation. IEEE Trans. Comput., C-35(8):677–691, Aug. 1986.

    Google Scholar 

  6. R. E. Bryant. Binary decision diagrams and beyond: Enabling technologies for formal verification. In IEEE/ACM International Conference on Computer Aided Design, ICCAD, San Jose/CA, pages 236–243. IEEE CS Press, Los Alamitos, 1995.

    Google Scholar 

  7. A. Califano, G. Stolovitzky, and Y. Tu. Analysis of gene expression microarrays for phenotype classification. In Proc. Int Conf Intell Syst Mol Biol, pages 75–85, 2000.

    Google Scholar 

  8. W. Chen, M. Reiss, and D. J. Foran. A prototype for unsupervised analysis of tissue microarrays for cancer research and diagnostics. IEEE Trans Inf Technol Biomed, 8(2):89–96, 2004.

    Article  MATH  Google Scholar 

  9. Y. Cheng and G. M. Church. Biclustering of expression data. In Proceedings of ISMB, pages 93–103, 2000.

    Google Scholar 

  10. R. J. Cho, M. J. Campbell, E. A. Winzeler, L. Steinmetz, A. Conway, L. Wodicka, T. G. Wolfsberg, A. E. Gabrielian, D. Landsman, D. J. Lockhart, and R. W. Davis. A genomewide transcriptional analysis of the mitotic cell cycle. Mol. Cell, 2(1):65–73, July 1998.

    Article  Google Scholar 

  11. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms. The MIT Press, Cambridge, Massachusetts, 2001.

    MATH  Google Scholar 

  12. G. De Micheli. Synthesis and Optimization of Digital Circuits. McGraw-Hill, New York, 1994.

    Google Scholar 

  13. J. DeRisi, L. Penland, P. O. Brown, M. L. Bittner, P. S. Meltzer, M. Ray, Y. Chen, Y. A. Su, and J. M. Trent. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genetics, 14(4):457–460, 1996.

    Article  Google Scholar 

  14. S. Drăghici. Data Analysis Tools for DNA Microarrays. Chapman & Hall/CRC, Florida, 2003.

    Google Scholar 

  15. A. C. R. Grayson, R. S. Shawgo, A. M. Johnson, N. T. Flynn, Y. Li, M. J. Cima, and R. Langer. A BioMEMS review: MEMS technology for physiologically integrated devices. Proceedings of the IEEE, 92(1):6–21, 2004.

    Article  Google Scholar 

  16. D. Gusfield. Algorithms on String, Trees and Sequences: Computer Science and Computational Biology. Cambridge, New York, 1997.

    Google Scholar 

  17. A. Hassibi and T. H. Lee. A programmable electrochemical biosensor array in 0.18μm standard CMOS. In Proceedings of IEEE International Solid-State Circuits Conference, February 2005.

    Google Scholar 

  18. J. P. T. Higgins, R. Shinghal, H. Gill, J. H. Reese, M. Terris, R. J. Cohen, M. Fero, J. R. Pollack, M. van de Rijn, and J. D. Brooks. Gene expression patterns in renal cell carcinoma assessed by complementary DNA microarray. American Journal of Pathology, 162(3):925–932, March 2003.

    Google Scholar 

  19. S. Kiyonaka, K. Sada, I. Yoshimura, S. Shinkai, N. Kato, and I. Hamachi. Semi-wet peptide/protein array using supramolecular hydrogel. Nature Mater., 3(1):58–64, 2004.

    Article  Google Scholar 

  20. I. S. Kohane, A. T. Kho, and A. J. Butte. Microarrays for an Integrative Genomics. The MIT Press, Cambridge, Massachusetts, 2003.

    Google Scholar 

  21. J. Liu, J. Yang, and W. Wang. Biclustering in gene expression data by tendency. In Proceedings of CSB, pages 182–193, 2004.

    Google Scholar 

  22. D. Lockhart, H. Dong, M. Byrne, M. Follettie, M. Gallo, M. Chee, M. Mittmann, C. Wang, M. Kobayashi, H. Horton, and E. L. Brown. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnology, 14(13):1675–1680, 1996.

    Article  Google Scholar 

  23. S. C. Madeira and A. L. Oliveira. Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Computational Biology and Bioinformatics, 1(1):24–45, 2004.

    Article  Google Scholar 

  24. C. D. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, Massachusetts, 1999.

    MATH  Google Scholar 

  25. C. Meinel and T. Theobald. Algorithms and Data Structures in VLSI Design. Springer, Berlin, 1998.

    MATH  Google Scholar 

  26. S. Minato. Zero-suppressed BDDs for set manipulation in combinatorial problems. In IEEE/ACM Design Automation Conference, DAC, Dallas/TX, pages 272–277. ACM Press, New York, 1993.

    Chapter  Google Scholar 

  27. S. Minato. Binary Decision Diagrams and Applications for VLSI CAD. Kluwer, 1996.

    Google Scholar 

  28. S. Minato and H. Arimura. Combinatorial item set analysis based on zero-supressed BDDs. Hokkaido University Technical Report, December 2004.

    Google Scholar 

  29. T. M. Murali and S. Kasif. Extracting conserved gene expression motifs from gene expression data. In Proceedings of Pacific Symposium on Biocomputing, pages 77–88, 2003.

    Google Scholar 

  30. R. Peeters. The maximum edge biclique problem is NP-complete. Discrete Applied Math., 131(3):651–654, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  31. S. Raychaundhuri, P. D. Sutphin, J. T. Chang, and R. B. Altman. Basic microarray analysis: grouping and feature reduction. Trends in Biotechnology, 19(5):189–193, May 2001.

    Article  Google Scholar 

  32. J. A. Rice. Mathematical Statistics and Data Analysis. Duxbury Press, 1994.

    Google Scholar 

  33. A. Y. Rubina, E. I. Dementieva, A. A. Stomakhin, E. L. Darii, S. V. Pan’kov, V. E. Barsky, S. M. Ivanov, E. V. Konovalova, and A. D. Mirzabekov. Hydrogel-based protein microchips: manufacturing, properties, and applications. Biotechniques, 34(5):1008–1014, 2003.

    Google Scholar 

  34. T. Sasao and M. Fujita. Representations of Discrete Functions. Kluwer, Massachusetts, 1996.

    MATH  Google Scholar 

  35. M. Schienle, C. Paulus, A. Frey, F. Hofmann, B. Holzapfl, P. Schindler-Bauer, and R. Thewes. A fully electronic DNA sensor with 128 positions and in-pixel A/D conversion. IEEE Journal of Solid-State Circuits, 39(12):2438–2445, 2004.

    Article  Google Scholar 

  36. E. Scrivener, R. Barry, A. Platt, R. Calvert, G. Masih, P. Hextall, M. Soloviev, and J. Terrett. Peptidomics: A new approach to affinity protein microarrays. Proteomics, 3(2):122–128, 2003.

    Article  Google Scholar 

  37. I. S. Shergill, N. K. Shergill, M. Arya, and H. R. Patel. Tissue microarrays: a current medical research tool. Curr Med Res Opin., 20(5):707–712, 2004.

    Article  Google Scholar 

  38. A. Tanay, R. Sharan, and R. Shamir. Discovering statistically significant biclusters in gene expression data. Bioinformatics, 18:S136–S144, 2002.

    Google Scholar 

  39. S. Tavazoie, J. D. Hughes, M. J. Campbell, R. J. Cho, and G. M. Church. Systematic determination of genetic network architecture. Nature Genetics, 22:281–285, 1999.

    Article  Google Scholar 

  40. E. Verpoorte and N. F. de Rooij. Microfluidics meets MEMS. Proceedings of the IEEE, 91(6):930–953, 2003.

    Article  Google Scholar 

  41. C. C. Wang, R. P. Huang, M. Sommer, H. Lisoukov, R. Huang, Y. Lin, T. Miller, and J. Burke. Array-based multiplexed screening and quantitation of human cytokines and chemokines. J Proteome Res., 1(4):337–343, 2002.

    Article  MATH  Google Scholar 

  42. H. Wang, W. Wang, J. Yang, and P. S. Yu. Clustering by pattern similarity in large data sets. In Proceedings of ACM SIGMOD, pages 394–405, 2002.

    Google Scholar 

  43. C.-J. Wu, Y. Fu, T. M. Murali, and S. Kasif. Gene expression module discovery using gibbs sampling. Genome Informatics, 15(1):239–248, 2004.

    Google Scholar 

  44. J. Yang, H. Wang, W. Wang, and P. Yu. Enhanced biclustering on expression data. In Proc. IEEE 3rd Symposium on Bioinformatics and Bioengineering, pages 321–327, 2003.

    Google Scholar 

  45. S. Yoon, C. Nardini, L. Benini, and G. De Micheli. An application of zero-suppressed binary decision diagrams to clustering analysis of dna microarray data. In Proceedings of the 26th Annual International Conference of the IEEE EMBS, pages 2925–2928, September 2004.

    Google Scholar 

  46. S. Yoon, C. Nardini, L. Benini, and G. De Micheli. Enhanced pClustering and its applications to gene expression data. In Proc. IEEE 4th Symposium on Bioinformatics and Bioengineering, pages 275–282, May 2004.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Systems Laboratory, Stanford University, Stanford, CA, 94305, USA

    Sungroh Yoon

  2. Department of Electrical Engineering and Computer Science (DEIS), University of Bologna, Bologna, 40136, Italy

    Luca Benini

  3. Integrated Systems Center, Swiss Federal Institute of Technology (EPFL), Lausanne, CH-1015, Switzerland

    Giovanni De Micheli

Authors
  1. Sungroh Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luca Benini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giovanni De Micheli
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Duke University, Durham, NC, U.S.A.

    Krishnendu Chakrabarty

  2. Coventor Inc., Cambridge, MA, U.S.A.

    Jun Zeng

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer

About this chapter

Cite this chapter

Yoon, S., Benini, L., De Micheli, G. (2006). A PATTERN-MINING METHOD FOR HIGH-THROUGHPUT LAB-ON-A-CHIP DATA ANALYSIS. In: Chakrabarty, K., Zeng, J. (eds) Design Automation Methods and Tools for Microfluidics-Based Biochips. Springer, Dordrecht . https://doi.org/10.1007/1-4020-5123-9_14

Download citation

  • DOI: https://doi.org/10.1007/1-4020-5123-9_14

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-5122-7

  • Online ISBN: 978-1-4020-5123-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation