BackgroundTo improve the tedious task of reconstructing gene networks through testing
experimentally the possible interactions between genes, it becomes a trend
to adopt the automated reverse engineering procedure instead. Some
evolutionary algorithms have been suggested for deriving network parameters.
However, to infer large networks by the evolutionary algorithm, it is
necessary to address two important issues: premature convergence and high
computational cost. To tackle the former problem and to enhance the
performance of traditional evolutionary algorithms, it is advisable to use
parallel model evolutionary algorithms. To overcome the latter and to speed
up the computation, it is advocated to adopt the mechanism of cloud
computing as a promising solution: most popular is the method of MapReduce
programming model, a fault-tolerant framework to implement parallel
algorithms for inferring large gene networks.ResultsThis work presents a practical framework to infer large gene networks, by
developing and parallelizing a hybrid GA-PSO optimization method. Our
parallel method is extended to work with the Hadoop MapReduce programming
model and is executed in different cloud computing environments. To evaluate
the proposed approach, we use a well-known open-source software
GeneNetWeaver to create several yeast S. cerevisiae sub-networks
and use them to produce gene profiles. Experiments have been conducted and
the results have been analyzed. They show that our parallel approach can be
successfully used to infer networks with desired behaviors and the
computation time can be largely reduced.ConclusionsParallel population-based algorithms can effectively determine network
parameters and they perform better than the widely-used sequential
algorithms in gene network inference. These parallel algorithms can be
distributed to the cloud computing environment to speed up the computation.
By coupling the parallel model population-based optimization method and the
parallel computational framework, high quality solutions can be obtained
within relatively short time. This integrated approach is a promising way
for inferring large networks.
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