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Application of Random Matrix Theory to Analyze Biological Data

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Handbook of Data Intensive Computing

Abstract

The development of high-throughput biological techniques, such as, gene expression microarray [1, 2], mass spectrometry [3], single-nucleotide polymorphism (SNP) arrays [4], next generation sequencing [5], yeast two hybrid screening [6], and synthetic genetic arrays [7] makes it possible to generate genotypic, transcriptional, proteomic, and other measurements about cellular systems on a massive scale. The application of these high-throughput techniques may revolutionize all aspects of biological research.

The research of Luo and Srimani was partially supported by NSF Grant DBI-0960586.

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Author information

Authors and Affiliations

  1. School of Computing, Clemson University, Clemson, SC, USA

    Feng Luo & Pradip K. Srimani

  2. Institute for Environmental Genomics, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

Authors
  1. Feng Luo
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  2. Pradip K. Srimani
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  3. Jizhong Zhou
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Corresponding author

Correspondence to Pradip K. Srimani .

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Editors and Affiliations

  1. Dept. of Computer Science & Engineering, Florida Atlantic University, Boca Raton, 33431, Florida, USA

    Borko Furht

  2. LexisNexis, Boca Raton, 33487, Florida, USA

    Armando Escalante

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Luo, F., Srimani, P.K., Zhou, J. (2011). Application of Random Matrix Theory to Analyze Biological Data. In: Furht, B., Escalante, A. (eds) Handbook of Data Intensive Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1415-5_28

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  • DOI: https://doi.org/10.1007/978-1-4614-1415-5_28

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