The Problem of Finding the Sparsest Bayesian Network for an Input Data Set is NP-Hard

Betliński, Paweł; Ślęzak, Dominik

doi:10.1007/978-3-642-34624-8_3

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…In this model, the dependencies between genes are described by a directed acyclic graph. The Bayesian network model is superior to other models in terms of dealing with noise and prior knowledge, but it has high computational complexity ( Tan et al, 2011 ; Betliński and Ślęzak, 2012 ; Shi et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

RWRNET: A Gene Regulatory Network Inference Algorithm Using Random Walk With Restart

et al. 2020

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Inferring gene regulatory networks from expression data is essential in identifying complex regulatory relationships among genes and revealing the mechanism of certain diseases. Various computation methods have been developed for inferring gene regulatory networks. However, these methods focus on the local topology of the network rather than on the global topology. From network optimisation standpoint, emphasising the global topology of the network also reduces redundant regulatory relationships. In this study, we propose a novel network inference algorithm using Random Walk with Restart (RWRNET) that combines local and global topology relationships. The method first captures the local topology through three elements of random walk and then combines the local topology with the global topology by Random Walk with Restart. The Markov Blanket discovery algorithm is then used to deal with isolated genes. The proposed method is compared with several state-of-the-art methods on the basis of six benchmark datasets. Experimental results demonstrated the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%