Publications indexed in Web of Science (Core Collection)
1. H Chen, L Albergante, JY Hsu, CA Lareau, GL Bosco, J Guan, S Zhou, AN Gorban, DE Bauer, MJ Aryee, DM Langenau, A Zinovyev, JD Buenrostro, G-C Yuan, L Pinello, Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nature communications. 2019 Apr 23;10(1):1903. https://doi.org/10.1038/s41467-019-09670-4 (IF 2017 12.353, Q1 in Multidisciplinary Sciences).
2. EV Pankratova, AI Kalyakulina, SV Stasenko, SYu Gordleeva, IA Lazarevich, VB. Kazantsev, Neuronal synchronization enhanced by neuron-astrocyte interaction, Nonlinear Dynamics, 2019 97, 647-662, https://doi.org/10.1007/s11071-019-05004-7 (IF 2017 4.339, Q1 In Mathematics, Applied)
3. IY Tyukin, D Iudin, F Iudin, T. Tyukina, V. Kazantsev, I Muhina, AN Gorban, Simple model of complex dynamics of activity patterns in developing networks of neuronal cultures, PLoS One, 2019, 14(6), e0218304 https://doi.org/10.1371/journal.pone.0218304, (IF 2.766, Q1 in Multidiscplinary Sciences).
4. AN Gorban, VA Makarov, IY Tyukin. Symphony of high-dimensional brain, Physics of Life Reviews, Volume 29, July 2019, Pages 115-119, (IF 2017 13.783, Q1 in biology and biophysics, the most cited journal in these categories).
5. AN Gorban, VA Makarov, IY Tyukin, The unreasonable effectiveness of small neural ensembles in high-dimensional brain, Physics of Life Reviews, Volume 29, July 2019, Pages 55-88, https://doi.org/10.1016/j.plrev.2018.09.005 – (IF 2017 13.783, Q1 in biology and biophysics, the most cited journal in these categories).
6. AG Korotkov, AO Kazakov, TA Levanova, GV Osipov, The dynamics of ensemble of neuron-like elements with excitatory couplings, Communications in Nonlinear Science and Numerical Simulation 71 (2019), 38-49 – (IF 2017 3.181, Q1 in mathematics, applied, and mathematics, interdisciplinary applications).
7. AN Gorban, R Burton, I Romanenko, IY Tyukin, One-trial correction of legacy AI systems and stochastic separation theorems, Information Sciences 484 (2019) 237–254 – (IF 2016 4.832, Q1 in computer science, information systems).
8. IY Tyukin, AN Gorban, S Green, D Prokhorov, Fast Construction of Correcting Ensembles for Legacy Artificial Intelligence Systems: Algorithms and a Case Study. Information Sciences 485 (2019), 230-247 – (IF 2016 4.832, Q1 in computer science, information systems).
9. T Yakhno, M Drozdov, V Yakhno, Giant Water Clusters: Where Are They From? Int. J. Mol. Sci. 2019, 20, 1582; doi:10.3390/ijms20071582. (IF 4.183, Q1 in Chemistry).
10. A.N. Gorban, E.M. Mirkes, I.Y. Tukin, How deep should be the depth of convolutional neural networks: a backyard dog case study. Cognitive Computation, 2019, https://doi.org/10.1007/s12559-019-09667-7 (IF=4.287, Q1 in Computer science, artificial intelligence)
11. AN Gorban, Universal Lyapunov functions for non-linear reaction networks, Communications in Nonlinear Science and Numerical Simulation, 2019, https://doi.org/10.1016/j.cnsns.2019.104910 (IF=3.967, Q1 in Mathematics, applied, and mathematics, interdisciplinary applications).
12. O Kuzenkov, A Morozov. Towards the Construction of a Mathematically Rigorous Framework for the Modelling of Evolutionary Fitness. Bulletin of Mathematical Biology. 2019 Apr 4:1-26. (IF 2017 1.484, Q3 in Biology Mathematical).
13. T Yakhno, V Yakhno, A Study of the Structural Organization of Water and Aqueous Solutions by Means of Optical Microscopy, Crystals 9(1) (2019), 52; https://doi.org/10.3390/cryst9010052 – (IF 2017 2.144, Q2 in Crystallography and Materials Science, Multidisciplinary).
14. A.N. Gorban, A. Harel-Bellan, N. Morozova, A. Zinovyev, Basic, simple and extendable kinetic model of protein synthesis, Mathematical Biosciences and Engineering, 2019, 16(6), 6602. https://doi.org/10.3934/mbe.2019329 (IF= 1.313, Q3 in Mathematical & computational biology).
15. S.V. Sidorov, N.Yu. Zolotykh, On the Linear Separability of Random Points in the d -dimensional Spherical Layer and in the d -dimensional Cube, proceeding of IJCNN 2019 - International Joint Conference on Neural Networks, Budapest Hungary, 14-19 July 2019, paper N-19253.
16. O.Kuzenkov, A.Morozov, G.Kuzenkova, Recognition of patterns of optimal diel vertical migration of zooplankton using neural networks, proceeding of IJCNN 2019 - International Joint Conference on Neural Networks, Budapest Hungary, 14-19 July 2019, paper N-19332.
17. I.Sereda, S.Alekseev, A.Koneva, R.Kataev, G.Osipov, ECG Segmentation by Neural Networks: Errors and Correction, proceeding of IJCNN 2019 - International Joint Conference on Neural Networks, Budapest Hungary, 14-19 July 2019, paper N-19185
18. E.M.Mirkes, J. Allohibi, A.N. Gorban, Do Fractional Norms and Quasinorms Help to Overcome the Curse of Dimensionality?, proceeding of IJCNN 2019 - International Joint Conference on Neural Networks, Budapest Hungary, 14-19 July 2019, paper N-19331.
19. L. Albergante, J. Bac, A. Zinovyev, Estimating the effective dimension of large biological datasets using Fisher separability analysis, proceeding of IJCNN 2019 - International Joint Conference on Neural Networks, Budapest Hungary, 14-19 July 2019, paper N-19814.
20. I.Y.Tyukin, A.Gorban, B. Grechuk, Kernel Stochastic Separation Theorems and Separability Characterizations of Kernel Classifiers, proceeding of IJCNN 2019 - International Joint Conference on Neural Networks, Budapest Hungary, 14-19 July 2019, paper N-20219.
21. AN Gorban, A Golubkov, B Grechuk, EM Mirkes, IY Tyukin, Correction of AI systems by linear discriminants: Probabilistic foundations, Information Sciences 466 (2018), 303-322 – (IF 2016 4.832, Q1 in computer science, information systems).
22. AN Gorban, Model reduction in chemical dynamics: slow invariant manifolds, singular perturbations, thermodynamic estimates, and analysis of reaction graph, Current Opinion in Chemical Engineering 21 (2018), 48-59. – (IF 2016 3.403, Q1 in Engineering, Chemical)
23. J Lages, DL Shepelyansky, A Zinovyev, Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks. PLoS ONE, 13(1) (2018). https://doi.org/10.1371/journal.pone.0190812 - (IF 2016 2.806, Q1 in Multidisciplinary Sciences)
24. S Lobov, N Krilova, I Kastalskiy, V Kazantsev, V.A. Makarov, Latent Factors Limiting the Performance of sEMG-Interfaces. Sensors 2018, 18, 1122. https://doi.org/10.3390/s18041122 – (IF 2016 2.677, Q1 in Instruments and Instrumentation)
25. Naldi A., Hernandez C., Levy N., Stoll G., Monteiro P.T., Chaouiya C., Helikar T., Zinovyev A., Calzone L., Cohen-Boulakia S., Thieffry D., Paulevé L. The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks, Front. Physiol., 19 June 2018, https://doi.org/10.3389/fphys.2018.00680 (IF 2016 4.134, Q1 in Physiology)
26. N Levy, A Naldi, C Hernandez, G Stoll, D Thieffry, A Zinovyev, L Calzone, L Paulevé, Prediction of Mutations to Control Pathways Enabling Tumor Cell Invasion with the CoLoMoTo Interactive Notebook (Tutorial), Front. Physiol., 06 July 2018 | https://doi.org/10.3389/fphys.2018.00787 (IF 2016 4.134, Q1 in Physiology)
27. I.Y. Tyukin, A.N. Gorban, K.I. Sofeykov, I. Romanenko, Knowledge transfer between artificial intelligence systems, Frontiers in Neurorobotics 12 (2018), https://doi.org/10.3389/fnbot.2018.00049 (IF 2.606)
28. AN Gorban, N Cabukoǧlu, Mobility cost and degenerated diffusion in kinesis models, Ecological Complexity 36 (2018), 16-21. (IF 1.634)
29. AN Gorban, N Cabukoǧlu, Basic model of purposeful kinesis. Ecological Complexity, 33 (2018), 75–83. (IF 1.634)
30. AN Gorban, EM Mirkes, A Zinovyev, Data analysis with arbitrary error measures approximated by piece-wise quadratic PQSQ functions, Proceedings of IJCNN 2018, paper #18525.
31. CC Tapia, JAV-Atienza, I Kastalskiy, S DiezHermano, AS Jimenez, VA Makarov, Cognitive Neural Network Driving DoF-Scalable Limbs in Time-Evolving Situations, Proceedings of IJCNN 2018, paper #18786.
32. IY Tyukin, AN Gorban, D Prokhorov, S Green, Efficiency of Shallow Cascades for Improving Deep Learning AI Systems, Proceedings of IJCNN 2018, paper #18433.
33. S Meshkinfamfard, A Gorban, I Tyukin, Tackling Rare False-Positives in Face Recognition: A Case Study. In 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) 2018 Jun 28 (pp. 1592-1598). IEEE.
34. I Tyukin, AN Gorban, C Calvo, J Makarova, VA Makarov, High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons. Bulletin of Mathematical Biology, 2018 1–33. https://doi.org/10.1007/s11538-018-0415-5 (IF 2016 1.26).
1. AN Gorban New universal Lyapunov functions for non-linear reaction networks, https://arxiv.org/pdf/1902.05351.pdf
2. L Albergante, EM Mirkes, H Chen, A Martin, L Faure, E Barillot, L Pinello, AN Gorban, A Zinovyev, Robust and scalable learning of data manifolds with complex topologies via ElPiGraph. https://arxiv.org/abs/1804.07580.
4. A.N. Gorban, B. Grechuk, I.Y. Tyukin, Augmented Artificial Intelligence: a Conceptual Framework, https://arxiv.org/abs/1802.02172
5. IA Lazarevich, SS Stasenko, MA Rozhnova, EV Pankratova, AE Dityatev, VB Kazantsev, Dynamics of the brain extracellular matrix governed by interactions with neural cells, https://arxiv.org/abs/1807.05740.
6. H Chen, L Albergante, JY Hsu, CA Lareau, GL Bosco, J Guan, S Zhou, AN Gorban, DE Bauer, MJ Aryee, DM Langenau, A Zinovyev, JD Buenrostro, G-C Yuan, L Pinello, STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data. https://www.biorxiv.org/content/early/2018/04/18/302554.
7. T.A. Yakhno, V.G. Yakhno, A study of structural organization of water and aqueous solutions by means of optical microscopy, https://arxiv.org/abs/1809.00906.