2-years impact-factor Math-Net.Ru of «Matematicheskaya Biologiya i Bioinformatika» journal, 2021

2-years impact-factor Math-Net.Ru of the journal in 2021 is calculated as the number of citations in 2021 to the scientific papers published during 2019–2020.

The table below contains the list of citations in 2021 to the papers published in 2019–2020. We take into account all citing publications we found from different sources, mostly from references lists available on Math-Net.Ru. Both original and translation versions are taken into account.

The impact factor Math-Net.Ru may change when new citations to a year given are found.

1 2 3  Full list
N	Citing pulication		Cited paper
1.	E. Ya. Frisman, O. L. Zhdanova, M. P. Kulakov, G. P. Neverova, O. L. Revutskaya, “Mathematical modeling of population dynamics based on recurrent equations: results and prospects. Part II”, Biol. Bull, 48:3 (2021), 239–250	→	Modeling the spatio-temporal dynamics of a population with age structure and long-range interactions: synchronization and clustering M. P. Kulakov, E. Ya. Frisman Mat. Biolog. Bioinform., 14:1 (2019),  1–18
2.	V. Shanin, A. Juutinen, A. Ahtikoski, P. Frolov, O. Chertov, J. Ramo, A. Lehtonen, R. Laiho, P. Makiranta, M. Nieminen, A. Lauren, S. Sarkkola, T. Penttila, B. Tupek, R. Makipaa, “Simulation modelling of greenhouse gas balance in continuous-cover forestry of Norway spruce stands on nutrient-rich drained peatlands”, For. Ecol. Manage., 496 (2021), 119479	→	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., 14:1 (2019),  54–76
3.	V I Lisitsyn, N N Matveev, V V Saushkin, “Ecological and physiological modelling of mixed stand dynamics”, IOP Conf. Ser.: Earth Environ. Sci., 875:1 (2021), 012042	→	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., 14:1 (2019),  54–76
4.	R A Pratama, M F V Ruslau, Nurhayati, “Global Analysis of Stage Structure Two Predators Two Prey Systems Under Harvesting Effect for Mature Predators”, J. Phys.: Conf. Ser., 1899:1 (2021), 012099	→	Modeling the dynamics of predator-prey community with age structures G. P. Neverova, O. L. Zhdanova, E. Ya. Frisman Mat. Biolog. Bioinform., 14:1 (2019),  77–93
5.	V. Bystrov, A. Sidorova, A. Lutsenko, D. Shpigun, E. Malyshko, A. Nuraeva, P. Zelenovskiy, S. Kopyl, A. Kholkin, “Modeling of self-assembled peptide nanotubes and determination of their chirality sign based on dipole moment calculations”, Nanomaterials, 11:9 (2021), 2415	→	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., 14:1 (2019),  94–125
6.	V. S. Bystrov, J. Coutinho, O. A. Zhulyabina, S. A. Kopyl, P. S. Zelenovskiy, A. S. Nuraeva, V. A. Tverdislov, S. V. Filippov, A. L. Kholkin, V. Ya. Shur, “Modeling and physical properties of diphenylalanine peptide nanotubes containing water molecules”, Ferroelectrics, 574:1 (2021), 78–91	→	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., 14:1 (2019),  94–125
7.	I. V. Likhachev, V. S. Bystrov, “Sborka fenilalaninovoi nanotrubki molekulyarno-dinamicheskim manipulyatorom”, Matem. biologiya i bioinform., 16:2 (2021), 244–255	→	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., 14:1 (2019),  94–125
8.	N. S. Fialko, M. M. Olshevets, V. D. Lakhno, “Ravnovesnoe raspredelenie zaryada v konechnoi tsepochke s lovushkoi”, Matem. biologiya i bioinform., 16:1 (2021), 152–168	→	Numerical simulation of small radius polaron in a chain with random perturbations N. S. Fialko, V. D. Lakhno Mat. Biolog. Bioinform., 14:1 (2019),  126–136
9.	N. V. Pertsev, V. A. Topchii, K. K. Loginov, “Numerical modelling of the transition of infected cells and virions between two lymph nodes in a stochastic model of HIV-1 infection”, Russ. J. Numer. Anal. Math. Model, 36:5 (2021), 293–302	→	Stochastic Modeling of Compartmental Systems with Pipes K. K. Loginov, N. V. Pertsev, V. A. Topchii Mat. Biolog. Bioinform., 14:1 (2019),  188–203
10.	G. A. Bocharov, K. K. Loginov, N. V. Pertsev, V. A. Topchii, “Pryamoe statisticheskoe modelirovanie dinamiki VICh-1 infektsii na osnove nemarkovskoi stokhasticheskoi modeli”, Zh. vychisl. matem. i matem. fiz., 61:8 (2021), 1245–1268	→	Stochastic Modeling of Compartmental Systems with Pipes K. K. Loginov, N. V. Pertsev, V. A. Topchii Mat. Biolog. Bioinform., 14:1 (2019),  188–203
11.	O. L. Revutskaya, M. P. Kulakov, E. Ya. Frisman, “Vliyanie iz'yatiya na dinamiku chislennosti soobschestva «khischnik–zhertva» s uchetom vozrastnoi struktury zhertvy”, Kompyuternye issledovaniya i modelirovanie, 13:4 (2021), 823–844	→	Bistability and bifurcations in modified Nicholson–Bailey model with age-structure for prey O. L. Revutskaya, M. P. Kulakov, E. Ya. Frisman Mat. Biolog. Bioinform., 14:1 (2019),  257–278
12.	L. V. Yakushevich, L. A. Krasnobaeva, “Double energy profile of pBR322 plasmid”, AIMS Biophys., 8:2 (2021), 221–232	→	Plasmid pBR322 and nonlinear conformational distortions (kinks) L. V. Yakushevich, L. A. Krasnobaeva Mat. Biolog. Bioinform., 14:1 (2019),  327–339
13.	A. A. Grinevich, I. S. Masulis, L. V. Yakushevich, “Mathematical Modeling of Transcription Bubble Behavior in the pPF1 Plasmid and its Modified Versions: The Link between the Plasmid Energy Profile and the Direction of Transcription”, BIOPHYSICS, 66:2 (2021), 209	→	Plasmid pBR322 and nonlinear conformational distortions (kinks) L. V. Yakushevich, L. A. Krasnobaeva Mat. Biolog. Bioinform., 14:1 (2019),  327–339
14.	Ludmila V. Yakushevich, Larisa A. Krasnobaeva, “Ideas and methods of nonlinear mathematics and theoretical physics in DNA science: the McLaughlin-Scott equation and its application to study the DNA open state dynamics”, Biophys Rev, 13:3 (2021), 315	→	Plasmid pBR322 and nonlinear conformational distortions (kinks) L. V. Yakushevich, L. A. Krasnobaeva Mat. Biolog. Bioinform., 14:1 (2019),  327–339
15.	I. R. Akberdin, I. N. Kiselev, S. S. Pintus, R. N. Sharipov, A. Yu. Vertyshev, O. L. Vinogradova, D. V. Popov, F. A. Kolpakov, “A modular mathematical model of exercise-induced changes in metabolism, signaling, and gene expression in human skeletal muscle”, Int. J. Mol. Sci., 22:19 (2021), 10353	→	A modular visual model of energy metabolism in human skeletal muscle I. N. Kiselev, I. R. Akberdin, A. Vertyshev, D. V. Popov, F. A. Kolpakov Mat. Biolog. Bioinform., 14:2 (2019),  373–392
16.	V. A. Likhoshvai, T. M. Khlebodarova, “Evolution and extinction can occur rapidly: a modeling approach”, PeerJ, 9 (2021), e11130	→	A modular visual model of energy metabolism in human skeletal muscle I. N. Kiselev, I. R. Akberdin, A. Vertyshev, D. V. Popov, F. A. Kolpakov Mat. Biolog. Bioinform., 14:2 (2019),  373–392
17.	A. N. Korshounova, V. D. Lakhno, “Charge motion along polynucleotide chains in a constant electric field depends on the charge coupling constant with chain displacements”, Matem. biologiya i bioinform., 16:2 (2021), 411–421	→	Dynamics of large radius polaron in a model polynucleotide chain with random perturbations N. S. Fialko, V. D. Lakhno Mat. Biolog. Bioinform., 14:2 (2019),  406–419
18.	E. A. Isaev, F. A. Doronin, A. G. Evdokimov, D. V. Pervukhin, Yu. V. Rudyak, G. O. Rytikov, V. V. Kornilov, V. G. Nazarov, “Kolichestvennaya otsenka razvitiya kolonii mikromitsetov na poverkhnostyakh polimerov i kompozitov na ikh osnove”, Matem. biologiya i bioinform., 16:2 (2021), 367–379	→	Platelet adhesion quantification to fluorinated polyethylene from the structural caracteristics of its surface E. A. Isaev, D. V. Pervukhin, V. V. Kornilov, P. A. Tarasov, A. A. Grigor'ev, Yu. V. Rudyak, G. O. Rytikov, V. G. Nazarov Mat. Biolog. Bioinform., 14:2 (2019),  420–429
19.	F.A. Doronin, Yu.V. Rudyak, G.O. Rytikov, A.G. Evdokimov, V.G. Nazarov, “3D-printed planar microfluidic device on oxyfluorinated PET-substrate”, Polymer Testing, 99 (2021), 107209	→	Platelet adhesion quantification to fluorinated polyethylene from the structural caracteristics of its surface E. A. Isaev, D. V. Pervukhin, V. V. Kornilov, P. A. Tarasov, A. A. Grigor'ev, Yu. V. Rudyak, G. O. Rytikov, V. G. Nazarov Mat. Biolog. Bioinform., 14:2 (2019),  420–429
20.	O.S. Brusov, O.V. Senko, M.S. Kodryan, A.V. Kuznetsova, I.A. Matveev, I.V. Oleichik, N.S. Karpova, M.I. Faktor, A.V. Aleshenko, S.V. Sizov, “Application of machine learning for predicting the outcome of treatment of patients with schizophrenia according to the indicators of «Thrombodynamics» test”, Z. nevrol. psikhiatr. im. S.S. Korsakova, 121:8 (2021), 45	→	On possibility of machine learning application for diagnosing dementia by EEG signals I. V. Dorovskih, O. V. Sen'ko, V. Ya. Chuchupal, A. A. Dokukin, A. V. Kuznetsova Mat. Biolog. Bioinform., 14:2 (2019),  543–553
1 2 3  Full list