Qixia Yuan
Title
Cited by
Cited by
Year
ASSA-PBN: an approximate steady-state analyser of probabilistic Boolean networks
A Mizera, J Pang, Q Yuan
International Symposium on Automated Technology for Verification and …, 2015
182015
Taming asynchrony for attractor detection in large Boolean networks
A Mizera, J Pang, H Qu, Q Yuan
IEEE/ACM transactions on computational biology and bioinformatics 16 (1), 31-42, 2018
162018
Improving BDD-based attractor detection for synchronous Boolean networks
Q Yuan, H Qu, J Pang, A Mizera
Science China Information Sciences 59 (8), 080101, 2016
142016
ASSA-PBN: A toolbox for probabilistic Boolean networks
A Mizera, J Pang, C Su, Q Yuan
IEEE/ACM Transactions on Computational Biology and Bioinformatics 15 (4 …, 2017
132017
ASSA-PBN 2.0: A Software Tool for Probabilistic Boolean Networks
A Mizera, J Pang, Q Yuan
International Conference on Computational Methods in Systems Biology, 309-315, 2016
132016
Reviving the two-state Markov chain approach
A Mizera, J Pang, Q Yuan
IEEE/ACM transactions on computational biology and bioinformatics 15 (5 …, 2017
82017
Reviving the two-state markov chain approach (technical report)
A Mizera, J Pang, Q Yuan
arXiv preprint arXiv:1501.01779, 2015
82015
A new decomposition method for attractor detection in large synchronous Boolean networks
A Mizera, J Pang, H Qu, Q Yuan
International Symposium on Dependable Software Engineering: Theories, Tools …, 2017
72017
Should we learn probabilistic models for model checking? A new approach and an empirical study
J Wang, J Sun, Q Yuan, J Pang
International Conference on Fundamental Approaches to Software Engineering, 3-21, 2017
72017
Fast simulation of probabilistic Boolean networks
A Mizera, J Pang, Q Yuan
International Conference on Computational Methods in Systems Biology, 216-231, 2016
62016
Parallel approximate steady-state analysis of large probabilistic Boolean networks
A Mizera, J Pang, Q Yuan
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 1-8, 2016
62016
Reviving the two-state Markov chain approach (Technical report)(2015)
A Mizera, J Pang, Q Yuan
Accessed on http://arxiv. org/abs/1501.01779, 0
6
Probabilistic model checking of the PDGF signaling pathway
Q Yuan, P Trairatphisan, J Pang, S Mauw, M Wiesinger, T Sauter
Transactions on Computational Systems Biology XIV, 151-180, 2012
52012
Learning probabilistic models for model checking: an evolutionary approach and an empirical study
J Wang, J Sun, Q Yuan, J Pang
International Journal on Software Tools for Technology Transfer 20 (6), 689-704, 2018
42018
GPU-accelerated steady-state computation of large probabilistic Boolean networks
A Mizera, J Pang, Q Yuan
Formal Aspects of Computing 31 (1), 27-46, 2019
32019
ASSA-PBN 3.0: Analysing Context-Sensitive Probabilistic Boolean Networks
A Mizera, J Pang, H Qu, Q Yuan
International Conference on Computational Methods in Systems Biology, 277-284, 2018
32018
Taming asynchrony for attractor detection in large Boolean networks (technical report)
A Mizera, J Pang, H Qu, Q Yuan
arXiv preprint arXiv:1704.06530, 2017
32017
A study of the PDGF signaling pathway with PRISM
Q Yuan, J Pang, S Mauw, P Trairatphisan, M Wiesinger, T Sauter
arXiv preprint arXiv:1109.1367, 2011
32011
Model-checking based approaches to parameter estimation of gene regulatory networks
A Mizera, J Pang, Q Yuan
2014 19th International Conference on Engineering of Complex Computer …, 2014
22014
A new decomposition-based method for detecting attractors in synchronous Boolean networks
Q Yuan, A Mizera, J Pang, H Qu
Science of Computer Programming 180, 18-35, 2019
12019
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