  | HORNE, R. J., MAUW, S., MIZERA, A., Stemper, A., & THOEMEL, J. (2023). Anomaly Detection Using Deep Learning Respecting the Resources on Board a CubeSat. Journal of Aerospace Information Systems, 1-14. doi:10.2514/1.i011232  Peer reviewed |
 | HORNE, R. J., MAUW, S., MIZERA, A., STEMPER, A., & THOEMEL, J. (2022). Autonomous Trustworthy Monitoring and Diagnosis of CubeSat Health (AtMonSat). European Space Agency. https://orbilu.uni.lu/handle/10993/55702 |
  | Hasan, C., HORNE, R. J., MAUW, S., & MIZERA, A. (2022). Cloud removal from satellite imagery using multispectral edge-filtered conditional generative adversarial networks. International Journal of Remote Sensing, 43 (5), 1881-1893. doi:10.1080/01431161.2022.2048915 Peer Reviewed verified by ORBi |
 | PAUL, S., SU, C., PANG, J., & MIZERA, A. (2020). An efficient approach towards the source-target control of Boolean networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17 (6), 1932-1945. doi:10.1109/TCBB.2019.2915081 Peer Reviewed verified by ORBi |
 | Yuan, Q., MIZERA, A., PANG, J., & Qu, H. (2019). A new decomposition-based method for detecting attractors in synchronous Boolean networks. Science of Computer Programming, 180, 18-35. doi:10.1016/j.scico.2019.05.001 Peer Reviewed verified by ORBi |
 | MIZERA, A., PANG, J., & Yuan, Q. (2019). GPU-accelerated steady-state computation of large probabilistic Boolean networks. Formal Aspects of Computing, 31 (1), 27-46. doi:10.1007/s00165-018-0470-6 Peer Reviewed verified by ORBi |
 | MIZERA, A., PANG, J., Qu, H., & Yuan, Q. (2019). Taming asynchrony for attractor detection in large Boolean networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16 (1), 31-42. doi:10.1109/TCBB.2018.2850901 Peer reviewed |
 | 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 |
 | MIZERA, A., PANG, J., Su, C., & Yuan, Q. (2018). ASSA-PBN: A Toolbox for Probabilistic Boolean Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15 (4), 1203-1216. doi:10.1109/TCBB.2017.2773477 Peer reviewed |
 | PAUL, S., Su, C., PANG, J., & MIZERA, A. (2018). A Decomposition-based Approach towards the Control of Boolean Networks. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM. doi:10.1145/3233547.3233550 Peer reviewed |
 | MIZERA, A., PANG, J., Qu, H., & Yuan, Q. (2018). ASSA-PBN 3.0: Analysing Context-Sensitive Probabilistic Boolean Networks. In Proceedings of the 16th International Conference on Computational Methods in Systems Biology (pp. 277-284). Springer Science & Business Media B.V. doi:10.1007/978-3-319-99429-1_16 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 |
 | Zarnowiec, P., MIZERA, A., Chrapek, M., Urbaniak, M., & Kaca, W. (2016). Chemometric analysis of attenuated total reflectance infrared spectra of Proteus mirabilis strains with defined structures of LPS. Innate Immunity, 22 (5), 325-335. doi:10.1177/1753425916647470 Peer Reviewed verified by ORBi |
 | 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 |
 | Chen, X., MIZERA, A., & PANG, J. (2015). Activity tracking: A new attack on location privacy. In Proceedings of the 3rd IEEE Conference on Communications and Network Security (CNS'15) (pp. 22-30). IEEE CS. Peer reviewed |
 | TRAIRATPHISAN, P., MIZERA, A., PANG, J., TANTAR, A.-A., & SAUTER, T. (01 July 2014). optPBN: An Optimisation Toolbox for Probabilistic Boolean Networks. PLoS ONE, 9 (7), 98001 (1-15. doi:10.1371/journal.pone.0098001 Peer Reviewed verified by ORBi |
 | 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 |
 | TRAIRATPHISAN, P., MIZERA, A., PANG, J., TANTAR, A.-A., SCHNEIDER, J., & SAUTER, T. (01 July 2013). Recent development and biomedical applications of probabilistic Boolean networks. Cell Communication and Signaling, 11 (46). doi:10.1186/1478-811X-11-46 Peer Reviewed verified by ORBi |
 | MIZERA, A., PANG, J., SAUTER, T., & TRAIRATPHISAN, P. (2013). Mathematical modelling of the Platelet-Derived Growth Factor (PDGF) signalling pathway. In Proceedings of 4th Workshop on Computational Models for Cell Processes (CompMod'13) (pp. 35). |
 | MIZERA, A., PANG, J., SAUTER, T., & TRAIRATPHISAN, P. (2013). A balancing act: Parameter estimation for biological models with steady-state measurements. In Proceedings of 11th Conference on Computational Methods in Systems Biology (CMSB'13) (pp. 253-254). Springer. Peer reviewed |
 | Czeizler, E., MIZERA, A., & Petre, I. (2012). A Boolean Approach for Disentangling the Roles of Submodules to the Global Properties of a Biomodel. Fundamenta Informaticae, 116 (1-4), 51-63. doi:10.3233/FI-2012-668 Peer Reviewed verified by ORBi |
 | Czeizler, E., MIZERA, A., Czeizler, E., Back, R.-J., Eriksson, J. E., & Petre, I. (2012). Quantitative analysis of the self-assembly strategies of intermediate filaments from tetrameric vimentin. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9 (3), 885 - 898. doi:10.1109/TCBB.2011.154 Peer Reviewed verified by ORBi |
 | MIZERA, A., Czeizler, E., & Petre, I. (2012). Self-assembly models of variable resolution. Lecture Notes in Computer Science, 7625, 181 - 203. doi:10.1007/978-3-642-35524-0_8 Peer reviewed |
 | MIZERA, A., Czeizler, E., & Petre, I. (2012). Computational methods for quantitative submodel comparison. In E. Katz (Ed.), Biomolecular Information Processing. From Logic Systems to Smart Sensors and Actuators (pp. 323 - 346). Weinheim, Germany: Wiley-VCH Verlag GmbH. doi:10.1002/9783527645480.ch17 Peer reviewed |
 | MIZERA, A. (2011). Methods for construction and analysis of computational models in systems biology: applications to the modelling of the heat shock response and the self-assembly of intermediate filaments [Doctoral thesis, Åbo Akademi University]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/22944 |
 | MIZERA, A., Czeizler, E., & Petre, I. (2011). Methods for Biochemical Model Decomposition and Quantitative Submodel Comparison. Israel Journal of Chemistry, 51 (1), 151–164. doi:10.1002/ijch.201000067 Peer Reviewed verified by ORBi |
 | Petre, I., MIZERA, A., Hyder, C. L., Meinander, A., Mikhailov, A., Morimoto, R. I., Sistonen, L., Eriksson, J. E., & Back, R.-J. (2011). A simple mass-action model for the eukaryotic heat shock response and its mathematical validation. Natural Computing, 10 (1), 595-612. doi:10.1007/s11047-010-9216-y Peer Reviewed verified by ORBi |
 | MIZERA, A., & Gambin, B. (2011). Modelling of ultrasound therapeutic heating and numerical study of the dynamics of the induced heat shock response. Communications in Nonlinear Science and Numerical Simulation, 16 (5), 2342–2349. doi:10.1016/j.cnsns.2010.04.056 Peer Reviewed verified by ORBi |
 | MIZERA, A., & Gambin, B. (2010). Stochastic modelling of the eukaryotic heat shock response. Journal of Theoretical Biology, 265 (3), 455–466. doi:10.1016/j.jtbi.2010.04.029 Peer Reviewed verified by ORBi |
 | Petre, I., MIZERA, A., & Back, R.-J. (2009). Computational heuristics for simplifying a biological model. Lecture Notes in Computer Science, 5635, 399-408. doi:10.1007/978-3-642-03073-4_41 Peer reviewed |
 | Petre, I., MIZERA, A., Hyder, C. L., Mikhailov, A., Eriksson, J. E., Sistonen, L., & Back, R.-J. (2009). A New Mathematical Model for the Heat Shock Response. In A. Condon, D. Harel, J. N. Kok, A. Salomaa, ... E. Winfree (Eds.), Algorithmic Bioprocesses (pp. 411-425). Berlin Heidelberg, Unknown/unspecified: Springer-Verlag. Peer reviewed |
 | Gambin, B., Kujawska, T., Kruglenko, E., MIZERA, A., & Nowicki, A. (2009). Temperature Fields Induced by Low Power Focused Ultrasound in Soft Tissues During Gene Therapy. Numerical Predictions and Experimental Results. Archives of Acoustics, 34 (4), 445–459. Peer reviewed |
 | MIZERA, A., & Gambin, B. (2009). The Dynamics of Heat Shock Response Induced by Ultr asound Therapeutic Treatment. In J. Awrejcewicz, M. Kaźmierczak, J. Mrozowski, ... P. Olejnik (Eds.), 10th Conference on Dynamical Systems – Theory and Applications, DSTA-2009 (pp. 847-852). Łódź, Poland: Left Grupa. Peer reviewed |
 | Norbert, D., Gambin, A., MIZERA, A., Wilczyński, B., & Tiuryn, J. (2006). Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics, 7, 249. doi:10.1186/1471-2105-7-249 Peer Reviewed verified by ORBi |