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   Algorithms for the Sequential Reprogramming of Boolean Networks; ; Pang, Jun  et alin IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019), 16(5), 1610-1619 Detailed reference viewed: 65 (1 UL)Proceedings of the 24th International Conference on Engineering of Complex Computer SystemsPang, Jun ; Book published by IEEE (2019) Detailed reference viewed: 23 (0 UL) Controlling large Boolean networks with temporary and permanent perturbations; Paul, Soumya ; Pang, Jun in Proceedings of the 23rd International Symposium on Formal Methods (FM'19) (2019) Detailed reference viewed: 80 (2 UL)A graph-based approach to explore relationship between hashtags and imagesZhong, Zhiqiang ; ; Pang, Jun in Proceedings of the 20th International Conference on Web Information System Engineering (2019) Detailed reference viewed: 67 (3 UL)Scalable control of asynchronous Boolean networks; Paul, Soumya ; Pang, Jun in Proceedings of 17th International Conference on Computational Methods in Systems Biology (CMSB'19) (2019) Detailed reference viewed: 65 (0 UL)An active learning-based approach for location-aware acquaintance inference; ; et al in Knowledge and Information Systems (2019), 59(3), 539-569 Detailed reference viewed: 102 (2 UL)Reviving the two-state Markov chain approachMizera, Andrzej ; Pang, Jun ; Yuan, Qixia 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: 132 (7 UL)Proceedings of the 12th International Symposium on Theoretical Aspects of Software EngineeringPang, Jun ; ; et alBook published by IEEE Computer Society (2018) Detailed reference viewed: 36 (2 UL)Learning probabilistic models for model checking: an evolutionary approach and an empirical study; ; et al in International Journal on Software Tools for Technology Transfer (2018), 20(6), 689-704 Detailed reference viewed: 93 (4 UL)L-CMP: an automatic learning-based parameterized verification tool; ; Pang, Jun in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (2018) Detailed reference viewed: 86 (1 UL)ASSA-PBN: A Toolbox for Probabilistic Boolean NetworksMizera, Andrzej ; Pang, Jun ; et alin IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018), 15(4), 1203-1216 Detailed reference viewed: 71 (4 UL)ASSA-PBN 3.0: Analysing Context-Sensitive Probabilistic Boolean NetworksMizera, Andrzej ; Pang, Jun ; et alin Proceedings of the 16th International Conference on Computational Methods in Systems Biology (2018) Detailed reference viewed: 102 (3 UL)On the Full Control of Boolean NetworksPaul, Soumya ; Pang, Jun ; in Proceedings of the 16th International Conference on Computational Methods in Systems Biology (2018) Detailed reference viewed: 89 (4 UL)Tagvisor: A privacy advisor for sharing hashtags; ; et al in Proceedings of The Web Conference 2018 (WWW'18) (2018) Detailed reference viewed: 36 (2 UL)A Decomposition-based Approach towards the Control of Boolean NetworksPaul, Soumya ; ; Pang, Jun et alin Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2018) Detailed reference viewed: 104 (4 UL)Towards the Existential Control of Boolean Networks: A Preliminary ReportPaul, Soumya ; Pang, Jun ; in Proceedings of the 4th International Symposium on Dependable Software Engineering. Theories, Tools, and Applications (2018) Detailed reference viewed: 65 (1 UL)An Automatic Proving Approach to Parameterized Verification; ; et al in ACM Transactions on Computational Logic (2018), 19(4), 1-27 Detailed reference viewed: 112 (1 UL)Does #like4like indeed provoke more likes?; ; et al in Proceedings of the 16th IEEE/WIC/ACM International Conference on Web Intelligence (WI'17) (2017) Detailed reference viewed: 96 (1 UL)walk2friends: Inferring Social Links from Mobility Profiles; ; Pang, Jun et alin Proceedings of the 24th ACM International Conference on Computer and Communications Security (2017) Detailed reference viewed: 89 (5 UL)Semantic annotation for places in LBSN through graph embedding; ; Pang, Jun et alin Proceedings of the 26th ACM International Conference on Information and Knowledge Management - CIKM'17 (2017) Detailed reference viewed: 101 (1 UL) |
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