Mining Discriminant Sequential Patterns for Aging Brain | SpringerLink
Skip to main content
Springer Nature Link
Log in

Mining Discriminant Sequential Patterns for Aging Brain

  • Conference paper
Artificial Intelligence in Medicine (AIME 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5651))

Included in the following conference series:

  • 2180 Accesses

Abstract

Discovering new information about groups of genes implied in a disease is still challenging. Microarrays are a powerful tool to analyse gene expression. In this paper, we propose a new approach outlining relationships between genes based on their ordered expressions. Our contribution is twofold. First, we propose to use a new material, called sequential patterns, to be investigated by biologists. Secondly, due to the expression matrice density, extracting sequential patterns from microarray datasets is far away from being easy. The aim of our proposal is to provide the biological experts with an efficient approach based on discriminant sequential patterns. Results of various experiments on real biological data highlight the relevance of our proposal.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Eleventh International Conference on Data Engineering, pp. 3–14. IEEE Computer Society Press, Los Alamitos (1995)

    Chapter  Google Scholar 

  2. Bellazzi, R., Zupan, B.: Towards knowledge-based gene expression data mining. Journal of Biomedical Informatics 40(6), 787–802 (2007)

    Article  CAS  PubMed  Google Scholar 

  3. Cong, G., Tung, A.K.H., Xu, X., Pan, F., Yang, J.: Farmer: Finding interesting rule groups in microarray datasets. In: SIGMOD Conference 2004, pp. 143–154. ACM, New York (2004)

    Google Scholar 

  4. Dong, G., Li, J.: Efficient mining of discriminant patterns: discovering trends and differences. In: KDD 1999: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 43–52. ACM, San Diego (1999)

    Chapter  Google Scholar 

  5. Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Science 85(25), 14863–14868 (1998)

    Article  Google Scholar 

  6. Khan, J., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., Meltzer, P.S.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7(6), 673–679 (2001)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Li, L., Weinberg, C.R., Darden, T.A., Pedersen, L.G.: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17(12), 1131–1142 (2001)

    Article  CAS  PubMed  Google Scholar 

  8. Pan, F., Cong, G., Tung, A.K.H., Yang, J., Zaki, M.J.: Carpenter: finding closed patterns in long biological datasets. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) KDD, pp. 637–642. ACM, New York (2003)

    Google Scholar 

  9. Piatetsky-Shapiro, G., Tamayo, P.: Microarray data mining: facing the challenges. SIGKDD Explor. Newsl. 5(2) (2003)

    Google Scholar 

  10. Sacchi, L., Larizza, C., Magni, P., Bellazzi, R.: Precedence Temporal Networks to represent temporal relationships in gene expression data. Journal of Biomedical Informatics 40(6), 761–774 (2007)

    Article  CAS  PubMed  Google Scholar 

  11. Sacchi, L., Larizza, C., Magni, P., Bellazzi, R.: Data mining with Temporal Abstractions: learning rules from time series. Data Min. Knowl. Discov. 15(2), 217–247 (2007)

    Article  Google Scholar 

  12. Salle, P., Bringay, S., Teisseire, M., Chakkour, F., Roche, M., Devau, G., Lautier, C., Verdier, J.M.: GeneMining: Identification, Visualization, and Interpretation of Brain Ageing Signatures. In: MIE 2009 Medical Informatics Europe, Sarajevo (2009)

    Google Scholar 

  13. Tanasa, D., López, A., Trousse, B.: Extracting Sequential Patterns for Gene Regulatory Expressions Profiles. In: López, J.A., Benfenati, E., Dubitzky, W. (eds.) KELSI 2004. LNCS, vol. 3303, pp. 46–57. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  14. Tusher, V., Tibshirani, R., Chu, C.: Significance Analysis of microarrays Applied to Ionizing Radiation Response. Proc. Nat’l Academy of Sciences 98(9), 5116–5121 (2001)

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIRMM, Montpellier 2 University, CNRS, 161 rue Ada, 34392, Montpellier, France

    Paola Salle & Sandra Bringay

  2. MIAP Dpt., Montpellier 3 University, Route de Mende, 34199, Montpellier, France

    Sandra Bringay

  3. Cemagref, UMR TETIS, 500 rue Jean-François Breton, F-34093, Montpellier, France

    Maguelonne Teisseire

Authors
  1. Paola Salle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandra Bringay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maguelonne Teisseire
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Verona, Department of Computer Science, Ca’ Vignal 2, strada le Grazie 15, 37134, Verona, Italy

    Carlo Combi

  2. Department of Information Systems Engineering, Ben Gurion University of the Negev, P.O. Box 653, 84105, Beer-Sheva, Israel

    Yuval Shahar

  3. Department of Medical Informatics, University of Amsterdam, Academic Medical Center, Meibergdreef 15, 1105, Amsterdam, AZ, The Netherlands

    Ameen Abu-Hanna

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Salle, P., Bringay, S., Teisseire, M. (2009). Mining Discriminant Sequential Patterns for Aging Brain. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02976-9_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02975-2

  • Online ISBN: 978-3-642-02976-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation