SlopeMiner: An Improved Method for Mining Subtle Signals in Time Course Microarray Data

  • Kevin McCormick
  • Roli Shrivastava
  • Li Liao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5059)


This paper presents an improved method, SlopeMiner, for analyzing time course microarray data by identifying genes that undergo gradual transitions in expression level. The algorithm calculates the slope for the slow transition between the expression levels of data, matching the sequence of expression level for each gene against temporal patterns having one transition between two expression levels. The method, when used along with StepMiner -an existing method for extracting binary signals, significantly increases the annotation accuracy.


Data mining Time Course DNA Microarray Regression 


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  1. 1.
    Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995)CrossRefGoogle Scholar
  2. 2.
    Lashkari, D.A., DeRisi, J.L., McCusker, J.H., Namath, A.F., Gentile, C., Hwang, S.Y., Brown, P.O.: Yeast microarray for genome wide parallel genetic and gene expression analysis. PNAS 94, 13057–13062 (1997)CrossRefGoogle Scholar
  3. 3.
    Aach, J., Church, G.M.: Aligning gene expression time series with time warping algorithms. Bioinformatics 17, 495–508 (2001)CrossRefGoogle Scholar
  4. 4.
    Amato, R., Ciaramella, A., Deniskina, N., Del Mondo, C., di Bernardo, D., Donalek, C., Longo, G., Miele, G., et al.: A multi-step approach to time series analysis and gene expression clustering. Bioinformatics 22, 589–596 (2006)CrossRefGoogle Scholar
  5. 5.
    Bar-Joseph, Z.: Analyzing time series gene expression data. Bioinformatics 20, 2493–2503 (2004)CrossRefGoogle Scholar
  6. 6.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Bostein, D.: Cluster analysis and display of genome-wide expression patterns. PNAS 95, 14863–14868 (1998)CrossRefGoogle Scholar
  7. 7.
    De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9, 67–103 (2002)CrossRefGoogle Scholar
  8. 8.
    Martin, S., Zhang, Z., Martino, A., Faulon, J.-L.: Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics 23, 866–874 (2007)CrossRefGoogle Scholar
  9. 9.
    Luan, Y., Li., H.: Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics 19, 474–482 (2003)CrossRefGoogle Scholar
  10. 10.
    Sahoo, D., Dill, D.L., Tibshirani, R., Plevritis, S.K.: Extracting binary signals from microarray time-course data. Nucleic Acids Research 35, 3705–3712 (2007)CrossRefGoogle Scholar
  11. 11.
    Owen, A.: Discussion: Multivariate adaptive regression splines. Ann. Stat. 19, 102–112 (1991)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kevin McCormick
    • 1
  • Roli Shrivastava
    • 1
  • Li Liao
    • 1
  1. 1.Computer and Information SciencesUniversity of DelawareNewarkUSA

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