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fastBMA: Scalable Network Inference and Transitive Reduction
  • Ling-Hong Hung
  • Kaiyuan Shi
  • Migao Wu
  • William Chad Young
  • Adrian E. Raftery
  • Ka Yee Yeung, University of Washington Tacoma
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Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (, as part of the updated networkBMA Bioconductor package ( and as ready-to-deploy Docker images (

open access
Citation Information
Ling-Hong Hung, Kaiyuan Shi, Migao Wu, William Chad Young, et al.. "fastBMA: Scalable Network Inference and Transitive Reduction" GigaScience Vol. 6 Iss. 10 (2017)
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