| 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 | 23 | 2018 |
| 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 | 23 | 2015 |
| 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 | 16 | 2017 |
| Improving BDD-based attractor detection for synchronous Boolean networks Q Yuan, H Qu, J Pang, A Mizera Science China Information Sciences 59 (8), 1-16, 2016 | 15 | 2016 |
| 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 | 14 | 2016 |
| 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 | 10 | 2017 |
| 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 | 9 | 2017 |
| Reviving the two-state Markov chain approach A Mizera, J Pang, Q Yuan IEEE/ACM transactions on computational biology and bioinformatics 15 (5 …, 2017 | 8 | 2017 |
| Reviving the two-state markov chain approach (technical report) A Mizera, J Pang, Q Yuan arXiv preprint arXiv:1501.01779, 2015 | 8 | 2015 |
| 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 | 7 | 2019 |
| Fast simulation of probabilistic Boolean networks A Mizera, J Pang, Q Yuan International Conference on Computational Methods in Systems Biology, 216-231, 2016 | 6 | 2016 |
| 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 | 6 | 2016 |
| 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 | |
| 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 | 5 | 2018 |
| 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 | 5 | 2012 |
| 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 | 4 | 2018 |
| 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 | 3 | 2019 |
| 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 | 3 | 2017 |
| 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 | 3 | 2011 |
| 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 | 2 | 2014 |
