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Robust Processing of Microarray Data by Independent Component Analysis

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

Microarray Data Processing is becoming a field of important activity for Signal Processing and Pattern Recognition areas, as the extraction and mining of meaningful data from large groupings of microarray patterns is of vital importance in Medicine, Genomics, Proteomics, Pharmacology, etc. In this paper emphasis is placed on studying and cataloging the nature of possible sources of corruption of microarray data and in establishing a pre-processing methodology for discriminating sources of corruption from microarray data (de-noising). We also discuss ways of precisely reconstructing original contributions (theoretically hybridized data) using ICA methods. Some classical examples are shown, and a discussion follows the presentation of results.

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References

  1. Amaratunga, D., Cabrera, J.: Exploration and Análisis of DNA Microarray and Protein Array Data, pp. 54–56. Wiley Interscience, Hoboken (2004)

    Google Scholar 

  2. Nguyen, D.H., Arpat, A.B., Wang, N., Carroll, R.J.: DNA Microarray Experiments: Biological and Technological Aspects. Biometrics 58, 701–717 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Irizarry, R.A., Hobbs, B., Collin, F., Beazer-Barclay, Y.D., Antonellis, K.J., Scherf, U., Speed, T.P.: Exploration, normalization and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2), 249–264 (2003)

    Article  MATH  Google Scholar 

  4. Li, C., Wong, W.H.: Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Nat. Acad. Sci. 98, 31–36 (2002)

    Article  Google Scholar 

  5. Kerr, M.K.: Linear Models for Microarray Data Analysis:Hidden Similarities and Differences. Journal of Computational Biology 10(6), 891–901 (2003)

    Article  MathSciNet  Google Scholar 

  6. Naef, F., Lim, D.A., Patil, N., Magnasco, M.: From features to expression: High density oligonucleotide arrays revisited. In: Proc. DIMACS Workshop on Analysis of Gene Expression Data (2001)

    Google Scholar 

  7. Vaidyanathan, P.P.: Genomics and Proteomics: A Signal Processor’s Tour. IEEE Circuits and Systems Magazine 4th quarter, 6–29 (2004)

    Google Scholar 

  8. Reference to be completed

    Google Scholar 

  9. Reference to be completed

    Google Scholar 

  10. Schena, M., Davis, R.W.: Genes, genomes and chips. In: Schena, M. (ed.) DNA Microarrays, pp. 1–16. Oxford University Press, Oxford (1999)

    Google Scholar 

  11. Lee, S.-I., Batzoglou, S.: Application of independent component analysis to microarrays. Genome Biology 4, R76.1–R76-21 (2003)

    Google Scholar 

  12. Miskin, PhD thesis, University of Cambridge (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo, s/n, 28660, Boadilla del Monte, Madrid, Spain

    Francisco Díaz, Raul Malutan, Pedro Gómez & Victoria Rodellar

  2. Dept. De Arquitectura y Tecnologia de Computadores, Universidad de Granada, Avenida Daniel Saucedo, E-18071, Granada, Spain

    Carlos G. Puntonet

Authors
  1. Francisco Díaz
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  2. Raul Malutan
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  3. Pedro Gómez
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  4. Victoria Rodellar
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  5. Carlos G. Puntonet
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Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

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© 2005 Springer-Verlag Berlin Heidelberg

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Díaz, F., Malutan, R., Gómez, P., Rodellar, V., Puntonet, C.G. (2005). Robust Processing of Microarray Data by Independent Component Analysis. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_129

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  • DOI: https://doi.org/10.1007/11494669_129

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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