Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory |
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Authors: | Feng Luo Yunfeng Yang Jianxin Zhong Haichun Gao Latifur Khan Dorothea K Thompson Jizhong Zhou |
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Institution: | (1) Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA;(2) Computer Science & Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA;(3) Department of Physics, Xiangtan University, Hunan, 411105, PR China;(4) Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083, USA;(5) School of Computing, Clemson University, Clemson, SC 29634, USA;(6) Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA;(7) Department of Botany and Microbiology, Insitute for Environmental Genomics, University of Oklahoma, Norman, OK 73019, USA |
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Abstract: | Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect
the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity
based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets.
This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited
for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been
widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded
in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable
to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray
profiles using random matrix theory. |
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