Skip to main content
SpringerLink
Log in

Supporting System for Detecting Pathologies

  • Conference paper
Advances in Computational Intelligence (IWANN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6692))

Included in the following conference series:

Abstract

Arrays CGH make possible the realization of tests on patients for the detection of mutations in chromosomal regions. Detecting these mutations allows to carry out diagnoses and to complete studies of sequencing in relevant regions of the DNA. The analysis process of arrays CGH requires the use of mechanisms that facilitate the data processing by specialized personnel since traditionally, a segmentation process is needed and starting from the segmented data, a visual analysis of the information is carried out for the selection of relevant segments. In this study a CBR system is presented as a supporting system for the extraction of relevant information in arrays CGH that facilitates the process of analysis and its interpretation.

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 84.99
Price excludes VAT (USA)
  • Available as 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

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.

Similar content being viewed by others

References

  1. Aha, D., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)

    Google Scholar 

  2. Akhbardeh, A., Nikhil, Koskinenb, P.E., Yli-Harja, O.: Towards the experimental evaluation of novel supervised fuzzy adaptive resonance theory for pattern classification. Pattern Recognition Letters 29(8), 1082–1093 (2008)

    Article  Google Scholar 

  3. Bajo, J., De Paz, J.F., Rodríguez, S., González, A.: A new clustering algorithm applying a hierarchical method neural network. Logic Journal of IGPL (in Press)

    Google Scholar 

  4. Breiman, L., Fried, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth International Group, Belmont (1984)

    MATH  Google Scholar 

  5. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1984)

    MATH  Google Scholar 

  6. Brown, P.O., Botstein, D.: Exploring the new world of the genome with DNA microarrays. Nature Genetics 21, 33–37 (1999)

    Article  Google Scholar 

  7. Chen, W., Erdogan, F., Ropers, H., Lenzner, S., Ullmann, R.: CGHPRO- a comprehensive data analysis tool for array CGH. BMC Bioinformatics 6(85), 299–303 (2005)

    Google Scholar 

  8. Cohen, W.W.: Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  9. De Haan, J.R., Bauerschmidt, S., van Schaik, R.C., Piek, E., Buydens, L.M.C., Wehrens, R.: Robust ANOVA for microarray data. Chemometrics and Intelligent Laboratory Systems 98(1), 38–44 (2009)

    Article  Google Scholar 

  10. Duda, R.O., Hart, P.: Pattern classification and Scene Analysis. John Wisley & Sons, New York (1973)

    MATH  Google Scholar 

  11. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)

    Google Scholar 

  12. Fritzke, B.: A growing neural gas network learns topologies. Advances in Neural Information Processing Systems 7, 625–632 (1995)

    Google Scholar 

  13. Fritzke, B.: Fast Learning with Incremental RBF Networks. Neural Processing Letters 1(1), 2–5 (1994)

    Article  Google Scholar 

  14. Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Applied Statistics 28, 100–108 (1979)

    Article  MATH  Google Scholar 

  15. Hofmann, W.A., Weigmann, A., Tauscher, M., Skawran, B., Focken, T., Buurman, R., Wingen, L.U., Schlegelberger, B., Steinemann, D.: Analysis of Array-CGH Data Using the R and Bioconductor Software Suite. Comparative and Functional Genomics, Article ID 201325 (2009)

    Google Scholar 

  16. Holmes, G., Hall, M., Prank, E.: Generating Rule Sets from Model Trees. In: Advanced Topics in Artificial Intelligence, vol. 1747/1999, pp. 1–12 (2007)

    Google Scholar 

  17. Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)

    Article  MATH  Google Scholar 

  18. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)

    Book  MATH  Google Scholar 

  19. Kim, S.Y., Nam, S.W., Lee, S.H., Park, W.S., Yoo, N.J., Lee, J.Y., Chung, Y.J.: ArrayCyGHt, a web application for analysis and visualization of array-CGH data. Bioinformatics 21(10), 2554–2555 (2005)

    Article  Google Scholar 

  20. Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics, 59–69 (1982)

    Google Scholar 

  21. Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)

    Book  MATH  Google Scholar 

  22. Brunelli, R.: Histogram Analysis for Image Retrieval. Pattern Recognition 34, 1625–1637 (2001)

    Article  MATH  Google Scholar 

  23. Lima, C.A.M., Coelho, A.L.V., Von Zuben, F.J.: Hybridizing mixtures of experts with support vector machines: Investigation into nonlinear dynamic systems identification. Information Sciences 177(10), 2049–2074 (2007)

    Article  Google Scholar 

  24. Lingjaerde, O.C., Baumbush, L.O., Liestol, K., Glad, I.K., Borresen-Dale, A.L.: CGH-explorer, a program for analysis of array-CGH data. Bioinformatics 21(6), 821–822 (2005)

    Article  Google Scholar 

  25. Mantripragada, K.K., Buckley, P.G., Diaz de Stahl, T., Dumanski, J.P.: Genomic microarrays in the spotlight. Trends Genetics 20(2), 87–94 (2004)

    Article  Google Scholar 

  26. Martinetz, T., Schulten, K.: A neural-gas network learns topologies. Artificial Neural Networks 1, 397–402 (1991)

    Google Scholar 

  27. Menten, B., Pattyn, F., De Preter, K., Robbrecht, P., Michels, E., Buysse, K., Mortier, G., De Paepe, A., van Vooren, S., Vermeesh, J., et al.: ArrayCGHbase: an analysis platform for comparative genomic hybridization microarrays. BMC Bioinformatics 6(124), 179–187 (2006)

    Google Scholar 

  28. Nguyena, M.H., Abbassa, H.A., Mckay, R.I.: A novel mixture of experts model based on cooperative coevolution. Neurocomputing 70, 155–163 (2006)

    Article  Google Scholar 

  29. Pinkel, D., Albertson, D.G.: Array comparative genomic hybridization and its applications in cancer. Nature Genetics 37, 11–17 (2005)

    Article  Google Scholar 

  30. Po, R.W., Guh, Y.Y., Yang, M.S.: A new clustering approach using data envelopment analysis. European Journal of Operational Research 199(1), 276–284 (2009)

    Article  MATH  Google Scholar 

  31. Quinlan, J.R.: C4.5: Programs For Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)

    Google Scholar 

  32. Rosa, P., Viara, E., Hupé, P., Pierron, G., Liva, S., Neuvial, P., Brito, I., Lair, S., Servant, N., Robine, N., Manié, E., Brennetot, C., Janoueix-Lerosey, I., Raynal, V., Gruel, N., Rouveirol, C., Stransky, N., Stern, M., Delattre, O., Aurias, A., Radvanyi, F., Barillot, E.: VAMP: Visualization and analysis of array-CGH, transcriptome and other molecular profiles Bioinformatics. Bioinformatics 22(17), 2066–2073 (2006)

    Article  Google Scholar 

  33. Saitou, N., Nie, M.: The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987)

    Google Scholar 

  34. Savitha, R., Suresh, S., Sundararajan, N.: A fully complex-valued radial basis function network and its learning algorithm. Int. Journal of Neural Systems. 19(4), 253–267 (2009)

    Article  Google Scholar 

  35. Smith, M.L., Marioni, J.C., Hardcastle, T.J., Thorne, N.P.: snapCGH: Segmentation, Normalization and Processing of aCGH Data Users’ Guide. Bioconductor (2006)

    Google Scholar 

  36. Wang, P., Young, K., Pollack, J., Narasimham, B., Tibshirani, R.: A method for callong gains and losses in array CGH data. Biostat. 6(1), 45–58 (2005)

    Article  MATH  Google Scholar 

  37. Willenbrock, H., Fridlyand, J.: A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics 21(22), 4084–4091 (2005)

    Article  Google Scholar 

  38. Xia, X., McClelland, M., Wang, Y.: WebArray, an online platform for microarray data analysis. BMC Bionformatics 6(306), 1737–1745 (2005)

    Google Scholar 

  39. Ylstra, B., Van den Ijssel, P., Carvalho, B., Meijer, G.: BAC to the future! or oligonucleotides: a perspective for microarray comparative genomic hybridization (array CGH). Nucleic Acids Research 34, 445–450 (2006)

    Article  Google Scholar 

  40. Yue, S., Wang, C.: The influence of serial correlation on the Mann-Whitney test for detecting a shift in median. Advances in Water Resources 25(3), 325–333 (2002)

    Article  Google Scholar 

  41. Zhanga, H., Lu, J.: Creating ensembles of classifiers via fuzzy clustering and deflection. Fuzzy Sets and Systems 161(13), 1790–1802 (2010)

    Article  MathSciNet  Google Scholar 

  42. Kruskal, W., Wallis, W.: Use of ranks in one-criterion variance analysis. Journal of American Statistics Association (1952)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Automation, University of Salamanca, Plaza de la Merced s/n, 37008, Salamanca, Spain

    Carolina Zato, Juan F. De Paz, Fernando de la Prieta & Beatriz Martín

Authors
  1. Carolina Zato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan F. De Paz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fernando de la Prieta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Beatriz Martín
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Departament d’Enginyeria Electrònica, Universitat Politècnica de Catalunya (UPC), Campus Nord, Edificio C4, c/ Gran Capità s/n, 08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada, C/ Periodista Daniel Saucedo Aranda, 18071, Granada, Spain

    Ignacio Rojas

  3. Departamento Tecnologia Eletrónica, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Gonzalo Joya

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zato, C., De Paz, J.F., de la Prieta, F., Martín, B. (2011). Supporting System for Detecting Pathologies. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_84

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21498-1_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21497-4

  • Online ISBN: 978-3-642-21498-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation