References of "Pang, Jun 50002807"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailAn efficient approach towards the source-target control of Boolean networks
Paul, Soumya UL; Su, Cui UL; Pang, Jun UL et al

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

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target ... [more ▼]

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimised if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach. [less ▲]

Detailed reference viewed: 26 (0 UL)
Full Text
Peer Reviewed
See detailControlling large Boolean networks with temporary and permanent perturbations
Su, Cui; Paul, Soumya UL; Pang, Jun UL

in Proceedings of the 23rd International Symposium on Formal Methods (FM'19) (2019)

Detailed reference viewed: 18 (0 UL)
Full Text
Peer Reviewed
See detailSequential reprogramming of Boolean networks made practical
Mandon, Hugues; Su, Cui; Haar, Stefan et al

in Proceedings of 17th International Conference on Computational Methods in Systems Biology (CMSB'19) (2019)

Detailed reference viewed: 20 (0 UL)
Full Text
Peer Reviewed
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

Detailed reference viewed: 55 (0 UL)
Full Text
Peer Reviewed
See detailAlgorithms for the Sequential Reprogramming of Boolean Networks
Mandon, Hugues; Su, Cui; Pang, Jun UL et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019), 16(5), 1610-1619

Detailed reference viewed: 13 (0 UL)
Full Text
Peer Reviewed
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

Detailed reference viewed: 16 (1 UL)
Full Text
Peer Reviewed
See detailScalable control of asynchronous Boolean networks
Su, Cui; Paul, Soumya UL; Pang, Jun UL

in Proceedings of 17th International Conference on Computational Methods in Systems Biology (CMSB'19) (2019)

Detailed reference viewed: 17 (0 UL)
Full Text
Peer Reviewed
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

Detailed reference viewed: 42 (0 UL)
Full Text
Peer Reviewed
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

Detailed reference viewed: 48 (2 UL)
Full Text
Peer Reviewed
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

Detailed reference viewed: 28 (1 UL)
Full Text
Peer Reviewed
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)

Detailed reference viewed: 43 (1 UL)
Full Text
Peer Reviewed
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)

Detailed reference viewed: 25 (1 UL)
Full Text
Peer Reviewed
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)

Detailed reference viewed: 41 (4 UL)
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)

Detailed reference viewed: 18 (2 UL)
Full Text
Peer Reviewed
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

Detailed reference viewed: 55 (4 UL)
Full Text
Peer Reviewed
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)

Detailed reference viewed: 44 (2 UL)
Full Text
Peer Reviewed
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

Detailed reference viewed: 59 (1 UL)
Full Text
Peer Reviewed
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), 15(5), 1525-1537

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 ▲]

Detailed reference viewed: 81 (4 UL)
Full Text
Peer Reviewed
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)

Detailed reference viewed: 50 (2 UL)
Full Text
Peer Reviewed
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)

Detailed reference viewed: 33 (1 UL)