fastBMA: Scalable Network Inference and Transitive Reduction

Hung, Ling‐Hong; Shi, Kaiyuan; Wu, Migao; Young, William Chad; Raftery, Adrian E.; Yeung, Ka Yee

doi:10.1101/099036

Cited by 7 publications

(7 citation statements)

References 40 publications

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“…PCIT (Reverter and Chan, 2008), C3NET (Altay and Emmert-Streib, 2010) Feature selection approaches MRNET (Meyer et al, 2007), MRNETB (Meyer et al, 2010), Genie3 (Huynh-Thu et al, 2010) Bayesian model averaging FastBMA (Hung et al, 2017) and BNFinder prior edge probabilities and TF-gene regulations were supplied where applicable.…”

Section: Inference Approach Methods Co-expression Algorithmsmentioning

confidence: 99%

Peer Review #2 of "Distributed Bayesian networks reconstruction on the whole genome scale (v0.1)"

2018

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Background. Bayesian networks are directed acyclic graphical models widely used to represent the probabilistic relationships between random variables. They have been applied in various biological contexts, including gene regulatory networks and protein-protein interactions inference. Generally, learning Bayesian networks from experimental data is NP-hard, leading to widespread use of heuristic search methods giving suboptimal results. However, in cases when the acyclicity of the graph can be externally ensured, it is possible to find the optimal network in polynomial time. While our previously developed tool BNFinder implements polynomial time algorithm, reconstructing networks with the large amount of experimental data still leads to computations on single CPU growing exceedingly. Results. In the present paper we propose parallelized algorithm designed for multi-core and distributed systems and its implementation in the improved version of BNFinder-tool for learning optimal Bayesian networks. The new algorithm has been tested on different simulated and experimental datasets showing that it has much better efficiency of parallelization than the previous version. BNFinder gives comparable results in terms of accuracy with respect to current state-of-the-art inference methods, giving significant advantage in cases when external information such as regulators list or prior edge probability can be introduced, particularly for datasets with static gene expression observations. Conclusions. We show that the new method can be used to reconstruct networks in the size range of thousands of genes making it practically applicable to whole genome datasets of prokaryotic systems and large components of eukaryotic genomes. Our benchmarking results on realistic datasets indicate that the tool should be useful to wide audience of researchers interested in discovering dependencies in their large-scale transcriptomic datasets.

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Section: Inference Approach Methods Co-expression Algorithmsmentioning

confidence: 99%

Peer Review #2 of "Distributed Bayesian networks reconstruction on the whole genome scale (v0.1)"

2018

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“…Variations of BMA (Bayesian model averaging) methods have been proposed to facilitate gene network inference. Examples include iBMA [52], ScanBMA [16] and fastBMA [17] for analyzing high dimensional gene expression data.…”

Section: Regression-based Methodsmentioning

confidence: 99%

Integration of multiple data sources for gene network inference using genetic perturbation data

Liang

Young

Hung

et al. 2017

Preprint

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Background The inference of gene regulatory networks is of great interest and has various applications. The recent advances in high-throughout biological data collection have facilitated the construction and understanding of gene regulatory networks in many model organisms. However, the inference of gene networks from large-scale human genomic data can be challenging. Generally, it is difficult to identify the correct regulators for each gene in the large search space, given that the high dimensional gene expression data only provides a small number of observations for each gene.Results We present a Bayesian approach integrating external data sources with knockdown data from human cell lines to infer gene regulatory networks. In particular, we assemble multiple data sources including gene expression data, genome-wide binding data, gene ontology, known pathways and use a supervised learning framework to compute prior probabilities of regulatory relationships. We show that our integrated method improves the accuracy of inferred gene networks. We apply our method to two different human cell lines, which illustrates the general scope of our method.Conclusions We present a flexible and systematic framework for external data integration that improves the accuracy of human gene network inference while retaining efficiency. Integrating various data sources of biological information also provides a systematic way to build on knowledge from existing literature.

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“…They have developed a greedy mechanism for picking appropriate models based on Occam’s window principle. Parallel implementation of ScanBMA named as fastBMA [54] is available from https://github.com/lhhunghimself/fastBMA.…”

Section: Parallel Algorithmsmentioning

confidence: 99%

Parallel Algorithms for Inferring Gene Regulatory Networks: A Review

Abbaszadeh

Khanteymoori

Azarpeyvand

2018

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: System biology problems such as whole-genome network construction from large-scale gene expression data are sophisticated and time-consuming. Therefore, using sequential algorithms are not feasible to obtain a solution in an acceptable amount of time. Today, by using massively parallel computing, it is possible to infer large-scale gene regulatory networks. Recently, establishing gene regulatory networks from large-scale datasets have drawn the noticeable attention of researchers in the field of parallel computing and system biology. In this paper, we attempt to provide a more detailed overview of the recent parallel algorithms for constructing gene regulatory networks. Firstly, fundamentals of gene regulatory networks inference and large-scale datasets challenges are given. Secondly, a detailed description of the four parallel frameworks and libraries including CUDA, OpenMP, MPI, and Hadoop is discussed. Thirdly, parallel algorithms are reviewed. Finally, some conclusions and guidelines for parallel reverse engineering are described.

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fastBMA: Scalable Network Inference and Transitive Reduction

Cited by 7 publications

References 40 publications

Peer Review #2 of "Distributed Bayesian networks reconstruction on the whole genome scale (v0.1)"

Peer Review #2 of "Distributed Bayesian networks reconstruction on the whole genome scale (v0.1)"

Integration of multiple data sources for gene network inference using genetic perturbation data

Parallel Algorithms for Inferring Gene Regulatory Networks: A Review

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