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See detailGPU-accelerated steady-state computation of large probabilistic Boolean networks
Mizera, Andrzej; Pang, Jun UL; Yuan, Qixia

in Formal Aspects of Computing (2019), 31(1), 27-46

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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 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 detailControlling large Boolean networks with single-step perturbations
Baudin, Alexis; Paul, Soumya UL; Su, Cui et al

in Bioinformatics (2019), 35(14), 558-567

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See detailAn active learning-based approach for location-aware acquaintance inference
Chen, Bo-Heng; Li, Cheng-Te; Chuang, Kun-Ta et al

in Knowledge and Information Systems (2019), 59(3), 539-569

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See detailReviving the two-state Markov chain approach
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018)

Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such ... [more ▼]

Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such systems. However, for large PBNs, which often arise in systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall. Secondly, we conduct an extensive experimental comparison of the extended two-state Markov chain approach and another approach based on the Skart method. We analyse the results with machine learning techniques and we show that statistically the two-state Markov chain approach has a better performance. Finally, we demonstrate the potential of the extended two-state Markov chain approach on a case study of a large PBN model of apoptosis in hepatocytes. [less ▲]

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See detailOn the Full Control of Boolean Networks
Paul, Soumya UL; Pang, Jun UL; Su, Cui

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

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See detailTowards the Existential Control of Boolean Networks: A Preliminary Report
Paul, Soumya UL; Pang, Jun UL; Su, Cui

in Proceedings of the 4th International Symposium on Dependable Software Engineering. Theories, Tools, and Applications (2018)

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See detailL-CMP: an automatic learning-based parameterized verification tool
Cao, Jialun; Li, Yongjian; Pang, Jun UL

in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (2018)

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See detailLearning probabilistic models for model checking: an evolutionary approach and an empirical study
Wang, Jingyi; Sun, Jun; Yuan, Qixia et al

in International Journal on Software Tools for Technology Transfer (2018), 20(6), 689-704

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See detailProceedings of the 12th International Symposium on Theoretical Aspects of Software Engineering
Pang, Jun UL; Zhang, Chenyi; He, Jifeng et al

Book published by IEEE Computer Society (2018)

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See detailASSA-PBN: A Toolbox for Probabilistic Boolean Networks
Mizera, Andrzej; Pang, Jun UL; Su, Cui et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018), 15(4), 1203-1216

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See detailA Decomposition-based Approach towards the Control of Boolean Networks
Paul, Soumya UL; Su, Cui; Pang, Jun UL et al

in Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2018)

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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 detailTagvisor: A privacy advisor for sharing hashtags
Zhang, Yang; Humbert, Mathias; Rahman, Tahleen et al

in Proceedings of The Web Conference 2018 (WWW'18) (2018)

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See detailAn Automatic Proving Approach to Parameterized Verification
Li, Yongjian; Duan, Kaiqiang; Jansen, David et al

in ACM Transactions on Computational Logic (2018), 19(4), 1-27

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See detailDeepCity: A Feature Learning Framework for Mining Location Check-Ins
Pang, Jun UL; Zhang, Yang

in Proceedings of the 11th International Conference on Web and Social Media (ICWSM'17) (2017)

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See detailQuantifying location sociality
Pang, Jun UL; Zhang, Yang

in Proc. 28th ACM Conference on Hypertext and Social Media - HT'17 (2017)

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See detailFormal modelling and analysis of receipt-free auction protocols in applied pi
Dong, Naipeng; Jonker, Hugo; Pang, Jun UL

in Computers & Security (2017), 65

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See detailShould We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study
Wang, Jingyi; Sun, Jun; Yuan, Qixia UL et al

in Proceedings of 20th International Conference on Fundamental Approaches to Software Engineering (2017)

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