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Gene Expression Imputation Techniques for Robust Post Genomic Knowledge Discovery

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Computational Intelligence in Medical Informatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 85))

Microarrays measure expression patterns of thousands of genes at a time, under same or diverse conditions, to facilitate faster analysis of biological processes. This gene expression data is being widely used for diagnosis, prognosis and tailored drug discovery. Microarray data, however, commonly contains missing values, which can have high impact on subsequent biological knowledge discovery methods. This has been catalyst for the manifest of different imputation algorithms, including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component Analysis (BPCA), Least Square Impute (LSImpute), Local Least Square Impute (LLSImpute) and K-Nearest Neighbour (KNN). This Chapter investigates the impact of missing values on post genomic knowledge discovery methods like, Gene Selection and Gene Regulatory Network (GRN) reconstruction. A framework for robust subsequent biological knowledge inference has been proposed which has shown significant improvements in the outcomes of Gene Selection and GRN reconstruction methods.

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Author information

Authors and Affiliations

  1. Monash University, Churchill VIC 3842, Australia

    Muhammad Shoaib Sehgal (Faculty of IT) & Iqbal Gondal (Faculty of IT)

  2. Victorian Bioinformatics Consortium, Clayton VIC 3800, Australia

    Muhammad Shoaib Sehgal (Faculty of IT), Iqbal Gondal (Faculty of IT) & Laurence Dooley (Faculty of IT)

Authors
  1. Muhammad Shoaib Sehgal
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  2. Iqbal Gondal
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  3. Laurence Dooley
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Editor information

Editors and Affiliations

  1. Buffalo Neuroimaging Analysis Center The Jacobs Neurological Institute University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14203, USA

    Arpad Kelemen

  2. Centre for Quantifiable Quality of Service in Communication Systems (Q2S) Centre of Excellence, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  3. Biostatistics Dept. University at Buffalo, The State University of New York, 252A2 Farber Hall, 3435 Main st., Buffalo, NY, 14214, USA

    Yulan Liang

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© 2008 Springer-Verlag Berlin Heidelberg

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Sehgal, M.S., Gondal, I., Dooley, L. (2008). Gene Expression Imputation Techniques for Robust Post Genomic Knowledge Discovery. In: Kelemen, A., Abraham, A., Liang, Y. (eds) Computational Intelligence in Medical Informatics. Studies in Computational Intelligence, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75767-2_9

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  • DOI: https://doi.org/10.1007/978-3-540-75767-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75766-5

  • Online ISBN: 978-3-540-75767-2

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