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- W3088392543 abstract "Accurate inference of gene regulatory networks from genome-scale expression data is of great significance. Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) is one of the popular inference tools that aim to accomplish this goal. Our target is to execute ARACNe in a parallel environment without redesigning its internal structure. In this paper, we present a parallel execution platform for ARACNe, pARACNE, that takes advantage of parallel computing without altering the original algorithm. We use Dialogue on Reverse Engineering Assessment and Methods (DREAM) challenge networks for evaluation and assessment of pARACNe. We observe that our parallel platform is able to infer networks close to the network inferred by ARACNe in significantly less amount of time. On comparing with the gold standard, pARACNe performs relatively better than ARACNe. Our idea is simple yet effective in producing networks similar to the outcomes of ARACNe in a cost-effective way." @default.
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- W3088392543 date "2020-09-22" @default.
- W3088392543 modified "2023-09-27" @default.
- W3088392543 title "pARACNE: A Parallel Inference Platform for Gene Regulatory Network Using ARACNe" @default.
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- W3088392543 doi "https://doi.org/10.1007/978-981-15-6067-5_11" @default.
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