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YUAN Qixia

Main Referenced Co-authors
PANG, Jun  (13)
MIZERA, Andrzej  (10)
Qu, Hongyang (3)
MAUW, Sjouke  (2)
SAUTER, Thomas  (2)
Main Referenced Keywords
attractor detection (1); binary decision diagram (1); Boolean networks (1); decomposition (1); long-run dynamics (1);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Computer science (14)
Biotechnology (1)

Publications (total 14)

The most downloaded
246 downloads
MIZERA, A., PANG, J., & YUAN, Q. (2018). Reviving the two-state Markov chain approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15 (5), 1525-1537. doi:10.1109/TCBB.2017.2704592 https://hdl.handle.net/10993/34366

The most cited

17 citations (Scopus®)

MIZERA, A., PANG, J., & YUAN, Q. (2015). ASSA-PBN: An approximate steady-state analyser for probabilistic Boolean networks. In Proceedings of the 13th International Symposium on Automated Technology for Verification and Analysis (ATVA'15) (pp. 214-220). Springer. https://hdl.handle.net/10993/22132

MIZERA, A., PANG, J., & YUAN, Q. (2018). Reviving the two-state Markov chain approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15 (5), 1525-1537. doi:10.1109/TCBB.2017.2704592
Peer Reviewed verified by ORBi

YUAN, Q. (2017). Computational Methods for Analysing Long-run Dynamics of Large Biological Networks [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/33749

Wang, J., Sun, J., YUAN, Q., & PANG, J. (2017). Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study. In Proceedings of 20th International Conference on Fundamental Approaches to Software Engineering (pp. 3-21). Springer.
Peer reviewed

MIZERA, A., PANG, J., Qu, H., & YUAN, Q. (2017). A new decomposition method for attractor detection in large synchronous Boolean networks. In Proceedings of the 3rd International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (pp. 232-249). Springer Science & Business Media B.V.
Peer reviewed

MIZERA, A., PANG, J., & YUAN, Q. (2016). Parallel Approximate Steady-state Analysis of Large Probabilistic Boolean Networks. In Proceedings of the 31st ACM Symposium on Applied Computing. ACM.
Peer reviewed

MIZERA, A., PANG, J., & YUAN, Q. (2016). Fast simulation of probabilistic Boolean networks. In Proceedings of 14th International Conference on Computational Methods in Systems Biology (pp. 216-231). Berlin, Germany: Springer.
Peer reviewed

MIZERA, A., PANG, J., & YUAN, Q. (2016). ASSA-PBN 2.0: A software tool for probabilistic Boolean networks. In Proceedings of 14th International Conference on Computational Methods in Systems Biology (pp. 309-315). Berlin, Germany: Springer.
Peer reviewed

MIZERA, A., PANG, J., & YUAN, Q. (2016). GPU-accelerated steady-state analysis of probabilistic Boolean networks [Poster presentation]. 14th International Conference on Computational Methods in Systems Biology.

YUAN, Q., Qu, H., PANG, J., & MIZERA, A. (2016). Improving BDD-based attractor detection for synchronous Boolean networks. Science China Information Sciences, 59 (8), 080101:1-080101:16. doi:10.1007/s11432-016-5594-9
Peer reviewed

MIZERA, A., PANG, J., & YUAN, Q. (2015). ASSA-PBN: An approximate steady-state analyser for probabilistic Boolean networks. In Proceedings of the 13th International Symposium on Automated Technology for Verification and Analysis (ATVA'15) (pp. 214-220). Springer.
Peer reviewed

Qu, H., YUAN, Q., PANG, J., & MIZERA, A. (2015). Improving BDD-based attractor detection for synchronous Boolean networks. In Proceedings of the 7th Asia-Pacific Symposium on Internetware. ACM.
Peer reviewed

MIZERA, A., PANG, J., & YUAN, Q. (2014). Model-checking based approaches to parameter estimation of gene regulatory networks. In Proceedings of 19th IEEE Conference on Engineering of Complex Computer Systems (pp. 206-209). IEEE CS. doi:10.1109/ICECCS.2014.38
Peer reviewed

YUAN, Q., TRAIRATPHISAN, P., PANG, J., MAUW, S., WIESINGER, M., & SAUTER, T. (2012). Probabilistic model checking of the PDGF signaling pathway. Transactions on Computational Systems Biology, XIV, 151-180.
Peer reviewed

YUAN, Q., PANG, J., MAUW, S., TRAIRATPHISAN, P., WIESINGER, M., & SAUTER, T. (2011). A study of the PDGF signaling pathway with PRISM. Proceedings of the 3rd Workshop on Computational Models for Cell Processes, EPTCS 67, 65-81.
Peer reviewed

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