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Most published authors (scientific articles only) of the journal
|
| 1. |
V. D. Lakhno |
49 |
| 2. |
M. N. Ustinin |
29 |
| 3. |
N. N. Nazipova |
22 |
| 4. |
S. D. Rykunov |
22 |
| 5. |
E. Ya. Frisman |
21 |
| 6. |
V. Yu. Lunin |
18 |
| 7. |
V. V. Panyukov |
17 |
| 8. |
D. A. Tikhonov |
17 |
| 9. |
G. P. Neverova |
16 |
| 10. |
O. N. Ozoline |
16 |
| 11. |
V. S. Bystrov |
15 |
| 12. |
T. E. Petrova |
15 |
| 13. |
A. M. Andrianov |
14 |
| 14. |
V. A. Kutyrkin |
14 |
| 15. |
V. A. Likhoshvai |
14 |
| 16. |
M. B. Chaley |
14 |
| 17. |
N. L. Lunina |
13 |
| 18. |
T. M. Khlebodarova |
13 |
| 19. |
A. I. Boyko |
12 |
| 20. |
N. V. Zaitseva |
12 |
| 21. |
E. A. Isaev |
12 |
| 22. |
A. N. Korshounova |
12 |
| 23. |
A. E. Medvedev |
12 |
| 24. |
P. V. Trusov |
12 |
| 25. |
A. V. Tuzikov |
12 |
| 26. |
N. S. Fialko |
12 |
| 27. |
A. I. Abakumov |
12 |
| 28. |
N. V. Pertsev |
12 |
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40 most published authors of the journal |
|
| Most cited authors of the journal |
| 1. |
V. D. Lakhno |
155 |
| 2. |
M. N. Ustinin |
115 |
| 3. |
E. Ya. Frisman |
92 |
| 4. |
D. A. Tikhonov |
83 |
| 5. |
G. P. Neverova |
74 |
| 6. |
V. S. Bystrov |
70 |
| 7. |
A. V. Efimov |
69 |
| 8. |
L. I. Kulikova |
68 |
| 9. |
N. N. Nazipova |
68 |
| 10. |
A. I. Abakumov |
68 |
| 11. |
S. D. Rykunov |
65 |
| 12. |
E. A. Isaev |
54 |
| 13. |
N. V. Zaitseva |
52 |
| 14. |
P. V. Trusov |
52 |
| 15. |
N. Adlakha |
49 |
| 16. |
M. Yu. Cinker |
49 |
| 17. |
A. N. Korshounova |
47 |
| 18. |
N. V. Pertsev |
43 |
| 19. |
R. Llinás |
42 |
| 20. |
O. N. Ozoline |
41 |
|
40 most cited authors of the journal |
|
| Most cited articles of the journal |
| 1. |
Nonlinear dynamic modeling of 2-dimensional interdependent calcium and inositol 1,4,5-trisphosphate in cardiac myocyte Nisha Singh, Neeru Adlakha Mat. Biolog. Bioinform., 2019, 14:1, 290–305 |
26 |
| 2. |
Modeling of human breath: conceptual and mathematical statements P. V. Trusov, N. V. Zaitseva, M. Yu. Tsinker Mat. Biolog. Bioinform., 2016, 11:1, 64–80 |
24 |
| 3. |
Simulation of buffered advection diffusion of calcium in a hepatocyte cell Y. D. Jagtap, N. Adlakha Mat. Biolog. Bioinform., 2018, 13:2, 609–619 |
23 |
| 4. |
Chiral peculiar properties of self-organization of diphenylalanine peptide nanotubes: modeling of structure and properties V. S. Bystrov, P. S. Zelenovskiy, A. S. Nuraeva, S. Kopyl, O. A. Zhulyabina, V. A. Tverdislov Mat. Biolog. Bioinform., 2019, 14:1, 94–125 |
18 |
| 5. |
Statistical analysis of the internal distances of helical pairs in protein molecules D. A. Tikhonov, L. I. Kulikova, A. V. Efimov Mat. Biolog. Bioinform., 2016, 11:2, 170–190 |
17 |
| 6. |
Mathematical models of tuberculosis extension and control of it (review) K. K. Avilov, A. A. Romanyukha Mat. Biolog. Bioinform., 2007, 2:2, 188–318 |
17 |
| 7. |
Autowave processes Yu. E. El'kin Mat. Biolog. Bioinform., 2006, 1:1, 27–40 |
17 |
| 8. |
Modeling of insect-pathogen dynamics with biological control S. Saha, G. Samanta Mat. Biolog. Bioinform., 2020, 15:2, 268–294 |
16 |
| 9. |
Computational studies of the hydroxyapatite nanostructures, peculiarities and properties V. S. Bystrov Mat. Biolog. Bioinform., 2017, 12:1, 14–54 |
16 |
| 10. |
The harvesting effect on a fish population A. I. Abakumov, Yu. G. Izrailsky Mat. Biolog. Bioinform., 2016, 11:2, 191–204 |
16 |
| 11. |
Numerical algorithms for diffusion coefficient identification in problems of tissue engineering A. V. Penenko, S. N. Nikolaev, S. Golushko, A. V. Romashenko, I. A. Kirilova Mat. Biolog. Bioinform., 2016, 11:2, 426–444 |
16 |
| 12. |
Software for the partial spectroscopy of human brain S. D. Rykunov, M. N. Ustinin, A. G. Polyanin, V. V. Sytchev, R. R. Llinás Mat. Biolog. Bioinform., 2016, 11:1, 127–140 |
15 |
| 13. |
Theoretical and experimental investigations of DNA open states A. S. Shigaev, O. A. Ponomarev, V. D. Lakhno Mat. Biolog. Bioinform., 2018, 13:Suppl., 162–267 |
14 |
| 14. |
On the DNA kink motion under the action of constant torque L. V. Yakushevich, V. N. Balashova, F. K. Zakiryanov Mat. Biolog. Bioinform., 2016, 11:1, 81–90 |
14 |
| 15. |
The study of interhelical angles in the structural motifs formed by two helices D. A. Tikhonov, L. I. Kulikova, A. V. Efimov Mat. Biolog. Bioinform., 2017, 12:1, 83–101 |
13 |
| 16. |
Dynamic modes of exploited limited population: results of modeling and numerical study G. P. Neverova, A. I. Abakumov, E. Ya. Frisman Mat. Biolog. Bioinform., 2016, 11:1, 1–13 |
13 |
| 17. |
Modeling of bacterial communication in the extended range of population dynamics Y. Shuai, A. G. Maslovskaya, C. Kuttler Mat. Biolog. Bioinform., 2023, 18:1, 89–104 |
12 |
| 18. |
A fractional epidemic model with Mittag-Leffler kernel for COVID-19 Hassan Aghdaoui, Mouhcine Tilioua, Kottakkaran Sooppy Nisar, Ilyas Khan Mat. Biolog. Bioinform., 2021, 16:1, 39–56 |
12 |
| 19. |
Dynamics of large radius polaron in a model polynucleotide chain with random perturbations N. S. Fialko, V. D. Lakhno Mat. Biolog. Bioinform., 2019, 14:2, 406–419 |
12 |
| 20. |
Effects of the аspen short-rotation plantation on the C and N biological cycles in boreal forests: the model experiment A. S. Komarov, O. G. Chertov, S. S. Bykhovets, I. V. Priputina, V. N. Shanin, E. O. Vidyagina, V. G. Lebedev, K. A. Shestibratov Mat. Biolog. Bioinform., 2015, 10:2, 398–415 |
12 |
| 21. |
Formation of stationary electronic states in finite homogeneous molecular chains V. D. Lakhno, A. N. Korshounova Mat. Biolog. Bioinform., 2010, 5:1, 1–29 |
12 |
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40 most cited articles of the journal |
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| Most requested articles of the journal |
|
|
| 1. |
Single particle study by X-ray diffraction: crystallographic approach V. Yu. Lunin, N. L. Lunina, T. E. Petrova Mat. Biolog. Bioinform., 2019, 14:2, 500–516 | 54 |
| 2. |
Parameterization of productivity model for the most common trees species in european part of Russia for simulation of forest ecosystem dynamics V. N. Shanin, P. Ya. Grabarnik, S. S. Bykhovets, O. G. Chertov, I. V. Priputina, M. P. Shashkov, N. V. Ivanova, M. N. Stamenov, P. V. Frolov, E. V. Zubkova, E. V. Ruchinskaya Mat. Biolog. Bioinform., 2019, 14:1, 54–76 | 41 |
| 3. |
Data center efficiency model: A new approach and the role of artificial intelligence E. A. Isaev, V. V. Kornilov, A. A. Grigor'ev Mat. Biolog. Bioinform., 2023, 18:1, 215–227 | 39 |
| 4. |
Functional classification of plants by multivariate analysis V. E. Smirnov Mat. Biolog. Bioinform., 2007, 2:1, 1–17 | 38 |
| 5. |
Joint use of different homogeneity testing criteria for latent periodicity revelation in biological sequences M. B. Chaley, N. N. Nazipova, V. A. Kutyrkin Mat. Biolog. Bioinform., 2007, 2:1, 20–35 | 37 |
| 6. |
Analysis of pine looper population dynamics using discrete time mathematical models L. V. Nedorezov Mat. Biolog. Bioinform., 2010, 5:2, 114–123 | 34 |
| 7. |
The use of connected masks for reconstructing the single particle image from X-ray diffraction data. III. Maximum-likelihood based strategies to select solution of the phase problem N. L. Lunina, T. E. Petrova, A. G. Urzhumtsev, V. Yu. Lunin Mat. Biolog. Bioinform., 2017, 12:2, 521–535 | 34 |
| 8. |
HEC 2.0: improved simulation of the evolution of prokaryotic communities S. A. Lashin, A. I. Klimenko, Z. S. Mustafin, N. A. Kolchanov, Yu. G. Matushkin Mat. Biolog. Bioinform., 2014, 9:2, 585–596 | 33 |
| 9. |
Trajectories of solitons movement in the potential field of pPF1 plasmid with non-zero initial velocity L. V. Yakushevich, L. A. Krasnobaeva Mat. Biolog. Bioinform., 2024, 19:1, 232–247 | 32 |
| 10. |
Rate calculation for metabolic reactions in a living and growing cell by the method of steady-state stoichiometric flux balance N. N. Nazipova, Yu. E. El'kin, V. V. Panyukov, L. N. Drozdov-Tikhomirov Mat. Biolog. Bioinform., 2007, 2:1, 98–119 | 32 |
|
| Total publications: |
644 |
| Scientific articles: |
640 |
| Authors: |
1008 |
| Citations: |
1650 |
| Cited articles: |
435 |
 |
Scopus Metrics |
|
2025 |
CiteScore |
0.900 |
|
2025 |
SNIP |
0.401 |
|
2025 |
SJR |
0.144 |
|
2024 |
CiteScore |
1.000 |
|
2024 |
SNIP |
0.494 |
|
2024 |
SJR |
0.143 |
|
2023 |
CiteScore |
1.100 |
|
2023 |
SNIP |
0.318 |
|
2023 |
SJR |
0.165 |
|
2022 |
SJR |
0.182 |
|
2021 |
SJR |
0.176 |
|
2020 |
SJR |
0.154 |
|
2019 |
SJR |
0.123 |
|
2018 |
CiteScore |
0.490 |
|
2018 |
SJR |
0.195 |
|
2017 |
CiteScore |
0.180 |
|
2017 |
SNIP |
0.121 |
|
2017 |
SJR |
0.136 |
|
2016 |
CiteScore |
0.220 |
|
2016 |
SNIP |
0.341 |
|
2016 |
SJR |
0.207 |
|
2015 |
CiteScore |
0.200 |
|
2015 |
SNIP |
0.217 |
|
2015 |
IPP |
0.148 |
|
2015 |
SJR |
0.128 |
|
2014 |
CiteScore |
0.160 |
|
2014 |
SNIP |
0.198 |
|
2014 |
IPP |
0.171 |
|
2014 |
SJR |
0.172 |
|
2013 |
SNIP |
0.041 |
|
2013 |
IPP |
0.063 |
|
2013 |
SJR |
0.126 |
|