Project publications
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).
27. T Yakhno, M Drozdov, V Yakhno, Giant Water Clusters:
Where Are They From? Int. J. Mol. Sci. 2019, 20, 1582; https://doi.org/10.3390/ijms20071582
. (IF 4.183, Q1 in Chemistry).
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).
Other
publications
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.
8.
S. Sidorov, On the 1-convexity of random points in the d-dimensional spherical layer,
https://arxiv.org/abs/1806.04732
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.