References of "Qu, Hongyang"
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See detailA new decomposition-based method for detecting attractors in synchronous Boolean networks
Yuan, Qixia; Mizera, Andrzej UL; Pang, Jun UL et al

in Science of Computer Programming (2019), 180

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See detailTaming asynchrony for attractor detection in large Boolean networks
Mizera, Andrzej; Pang, Jun UL; Qu, Hongyang et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019), 16(1), 31-42

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See detailASSA-PBN 3.0: Analysing Context-Sensitive Probabilistic Boolean Networks
Mizera, Andrzej; Pang, Jun UL; Qu, Hongyang et al

in Proceedings of the 16th International Conference on Computational Methods in Systems Biology (2018)

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See detailA new decomposition method for attractor detection in large synchronous Boolean networks
Mizera, Andrzej; Pang, Jun UL; Qu, Hongyang et al

in Proceedings of the 3rd International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (2017)

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See detailImproving BDD-based attractor detection for synchronous Boolean networks.
Yuan, Qixia UL; Qu, Hongyang; Pang, Jun UL et al

in SCIENCE CHINA Information Sciences (2016), 59(8), 0801011-08010116

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See detailImproving BDD-based attractor detection for synchronous Boolean networks
Qu, Hongyang; Yuan, Qixia UL; Pang, Jun UL et al

in Proceedings of the 7th Asia-Pacific Symposium on Internetware (2015)

Boolean networks are an important formalism for modelling biological systems and have attracted much attention in recent years. An important direction in Boolean networks is to exhaustively find ... [more ▼]

Boolean networks are an important formalism for modelling biological systems and have attracted much attention in recent years. An important direction in Boolean networks is to exhaustively find attractors, which represent steady states when a biological network evolves for a long term. In this paper, we propose a new approach to improve the efficiency of BDD-based attractor detection. Our approach includes a monolithic algorithm for small networks, an enumerative strategy to deal with large networks, and two heuristics on ordering BDD variables. We demonstrate the performance of our approach on a number of examples, and compare it with one existing technique in the literature. [less ▲]

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