Fast calculation of pairwise mutual information for gene regulatory network reconstruction.
We present a new software implementation to more efficiently compute the mutual information for all pairs of genes from gene expression microarrays. Computation of the mutual information is a necessary first step in various information theoretic approaches for reconstructing gene regulatory networks from microarray data. When the mutual information is estimated by kernel methods, computing the pairwise mutual information is quite time-consuming. Our implementation significantly reduces the computation time. For an example data set of 336 samples consisting of normal and malignant B-cells, with 9563 genes measured per sample, the current available software for ARACNE requires 142 hours to compute the mutual information for all gene pairs, whereas our algorithm requires 1.6 hours. The increased efficiency of our algorithm improves the feasibility of applying mutual information based approaches for reconstructing large regulatory networks.
Qiu
Peng
P
Department of Radiology, Stanford University, Stanford, CA, United States. qiupeng@stanford.edu
Gentles
Andrew J
AJ
Plevritis
Sylvia K
SK
D000465:Algorithms; D001185:Artificial Intelligence; D001402:B-Lymphocytes / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software