Publications indexed in Web of Science (Core Collection)
1. Golovenkin, S.E., Bac, J., Chervov, A., Mirkes, E.M., Orlova, Y.V., Barillot, E., Gorban, A.N. & Zinovyev, A. (2020). Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data. GigaScience. Volume 9, Issue 11, November 2020, giaa128, https://doi.org/10.1093/gigascience/giaa128 (IF 2019 5.993, Q1 in Multidisciplinary sciences)
2. Grechuk, B.; Gorban, A.N., Tyukin, I.Y. General stochastic separation theorems with optimal bounds, Neural Networks, 2021 (IF 2019 5.535, Q1 in computer science, artificial intelligence). https://doi.org/10.1016/j.neunet.2021.01.034
7. Bac J. and Zinovyev A., "Local intrinsic dimensionality estimators based on concentration of measure," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 2020, pp. 1-8, https://doi.org/10.1109/IJCNN48605.2020.9207096 .
8. Tyukin, I.Y.; Gorban, A.N.; McEwan, A.A.; Meshkinfamfard, S.; Tang, L. Blessing of dimensionality at the edge and geometry of few-shot learning, Information Sciences, 2021 ((IF 2019 5.910, Q1 in computer science, information systems). https://doi.org/10.1016/j.ins.2021.01.022.
9. Yakhno, T.; Pakhomov, A.; Sanin, A.; Kazakov, V.; Ginoyan, R.; Yakhno, V. Drop Drying on the Sensor: One More Way for Comparative Analysis of Liquid Media. Sensors 2020, 20, 5266. https://doi.org/10.3390/s20185266 (IF 2019 3.275, Q1 in Instruments & instrumentation )
10. Lazarevich I., Stasenko S., Rozhnova M., Pankratova E., Dityatev A., Kazantsev V. (2020) Activity-dependent switches between dynamic regimes of extracellular matrix expression. PLoS ONE 15(1): e0227917. https://doi.org/10.1371/journal.pone.0227917 (IF 2.766, Q1 in Multidiscplinary Sciences)
11. Gorban, A. N., Constales, D., & Yablonsky, G. S. (2020). Transient Concentration Extremum and Conservatively Perturbed Equilibrium. Chemical Engineering Science, 116295. https://doi.org/10.1016/j.ces.2020.116295 (IF 2019 3.871, Q1 in Engineering, chemical)
12. AN Gorban, VA Makarov, IY Tyukin, High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality, Entropy 22 (1), 82, https://doi.org/10.3390/e22010082 (IF 2019: 2.494, Q2 in PHYSICS, MULTIDISCIPLINARY)
13. J. Bac, A. Zinovyev. Lizard Brain: Tackling Locally Low-Dimensional Yet Globally Complex Organization of Multi-Dimensional Datasets, Front Neurorobot. 2020;13:110. https://doi.org/10.3389/fnbot.2019.00110 . (IF 2019: 2.574, Q2 in COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE)
14. L. Albergante, E. Mirkes, J. Bac, H. Chen, A. Martin, L. Faure, E. Barillot, L. Pinello, A. Gorban, A. Zinovyev, Robust and scalable learning of complex intrinsic dataset geometry via ElPiGraph. Entropy. 2020; 22(3), 296. https://doi.org/10.3390/e22030296 . (IF 2019: 2.494, Q2 in PHYSICS, MULTIDISCIPLINARY)
17. Mirkes, E.M., 2020. Universal Gorban’s Entropies: Geometric Case Study. Entropy, 22(3), p.264. https://doi.org/10.3390/e22030264 (IF 2019: 2.494, Q2 in PHYSICS, MULTIDISCIPLINARY)
18. 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).
19. J. Bac, A. Zinovyev. Local intrinsic dimensionality estimators based on concentration of measure, proceeding of WCCI2020, IJCNN 2020 –International Joint Conference on Neural Networks, Glasgow, UK, 19-24 July 2020, paper N-21312. (in press) https://arxiv.org/pdf/2004.04479.pdf
20. 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)
21. 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).
22. AN Gorban, VA Makarov, IY Tyukin. Symphony of high-dimensional brain, Physics of Life Reviews, Volume 29, July 2019, Pages 115-119, https://doi.org/10.1016/j.plrev.2019.06.003 (IF 2017 13.783, Q1 in biology and biophysics, the most cited journal in these categories).
23. 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).
24. 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. https://doi.org/10.1016/j.cnsns.2018.10.023 (IF 2017 3.181, Q1 in mathematics, applied, and mathematics, interdisciplinary applications).
25. 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. https://doi.org/10.1016/j.ins.2019.02.001 (IF 2016 4.832, Q1 in computer science, information systems).
26. 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. https://doi.org/10.1016/j.ins.2018.11.057 (IF 2016 4.832, Q1 in computer science, information systems).
28. 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)
29. 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).
30. 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). https://doi.org/10.1007/s11538-019-00602-3
31. 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).
32. 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).
33. 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. https://doi.org/10.1109/IJCNN.2019.8852237
34. 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. https://doi.org/10.1109/IJCNN.2019.8852060
35. 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. https://doi.org/10.1109/IJCNN.2019.8852106
36. 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. https://doi.org/10.1109/IJCNN.2019.8851899
37. 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. https://doi.org/10.1109/IJCNN.2019.8852450
38. 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. https://doi.org/10.1109/IJCNN.2019.8852278.
39. 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).
40. 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)
41. 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)
42. 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)
43. 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)
44. 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)
45. 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)
46. AN Gorban, N Cabukoǧlu, Mobility cost and degenerated diffusion in kinesis models, Ecological Complexity 36 (2018), 16-21. (IF 1.634)
47. AN Gorban, N Cabukoǧlu, Basic model of purposeful kinesis. Ecological Complexity, 33 (2018), 75–83. (IF 1.634)
48. 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.
49. 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.
50. IY Tyukin, AN Gorban, D Prokhorov, S Green, Efficiency of Shallow Cascades for Improving Deep Learning AI Systems, Proceedings of IJCNN 2018, paper #18433.
51. 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.
52. 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).
6. AN Gorban New universal Lyapunov functions for non-linear reaction networks, https://arxiv.org/pdf/1902.05351.pdf
7. 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.
9. A.N. Gorban, B. Grechuk, I.Y. Tyukin, Augmented Artificial Intelligence: a Conceptual Framework, https://arxiv.org/abs/1802.02172
10. 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.
11. 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.
12. 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.