Application of Random Matrix Theory to Analyze Biological Data

  • Feng Luo
  • Pradip K. Srimani
  • Jizhong Zhou


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.


Biological Network Correlation Matrice Random Matrix Theory Microarray Profile Saccharomyces Genome Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.School of ComputingClemson UniversityClemsonUSA
  2. 2.Institute for Environmental GenomicsUniversity of OklahomaNormanUSA

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