Mikhail Ivanchenko

3.1k total citations
98 papers, 1.8k citations indexed

About

Mikhail Ivanchenko is a scholar working on Statistical and Nonlinear Physics, Molecular Biology and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Mikhail Ivanchenko has authored 98 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Statistical and Nonlinear Physics, 32 papers in Molecular Biology and 29 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Mikhail Ivanchenko's work include Nonlinear Dynamics and Pattern Formation (21 papers), Nonlinear Photonic Systems (14 papers) and Neural dynamics and brain function (13 papers). Mikhail Ivanchenko is often cited by papers focused on Nonlinear Dynamics and Pattern Formation (21 papers), Nonlinear Photonic Systems (14 papers) and Neural dynamics and brain function (13 papers). Mikhail Ivanchenko collaborates with scholars based in Russia, Germany and United Kingdom. Mikhail Ivanchenko's co-authors include Sergej Flach, Oleg Kanakov, Grigory V. Osipov, Jürgen Kurths, V. D. Shalfeev, Claudio Franceschi, Igor Yusipov, T. V. Laptyeva, S. Denisov and Stefano Boccaletti and has published in prestigious journals such as Physical Review Letters, Nucleic Acids Research and PLoS ONE.

In The Last Decade

Mikhail Ivanchenko

95 papers receiving 1.8k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mikhail Ivanchenko Russia 24 856 583 399 395 356 98 1.8k
Alexey Zaikin United Kingdom 31 1.5k 1.7× 1.2k 2.0× 255 0.6× 614 1.6× 964 2.7× 146 3.2k
Govindan Rangarajan India 26 676 0.8× 369 0.6× 150 0.4× 953 2.4× 370 1.0× 82 2.7k
David Cai United States 28 1.1k 1.3× 393 0.7× 548 1.4× 736 1.9× 231 0.6× 106 2.3k
Dmitry E. Postnov Russia 21 1.1k 1.2× 1.1k 1.9× 161 0.4× 650 1.6× 268 0.8× 108 2.0k
T.D. Frank United States 29 1.9k 2.2× 637 1.1× 223 0.6× 728 1.8× 292 0.8× 180 3.2k
Shunsuke Sato Japan 24 582 0.7× 323 0.6× 84 0.2× 541 1.4× 202 0.6× 110 1.7k
Olga Sosnovtseva Russia 28 1.0k 1.2× 946 1.6× 205 0.5× 596 1.5× 540 1.5× 115 2.9k
Р. Р. Алиев Russia 20 349 0.4× 593 1.0× 141 0.4× 211 0.5× 264 0.7× 70 1.8k
Mathieu Desroches France 22 1.1k 1.3× 971 1.7× 105 0.3× 496 1.3× 207 0.6× 74 1.7k
А. Н. Павлов Russia 23 397 0.5× 410 0.7× 79 0.2× 774 2.0× 185 0.5× 181 2.0k

Countries citing papers authored by Mikhail Ivanchenko

Since Specialization
Citations

This map shows the geographic impact of Mikhail Ivanchenko's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mikhail Ivanchenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mikhail Ivanchenko more than expected).

Fields of papers citing papers by Mikhail Ivanchenko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mikhail Ivanchenko. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mikhail Ivanchenko. The network helps show where Mikhail Ivanchenko may publish in the future.

Co-authorship network of co-authors of Mikhail Ivanchenko

This figure shows the co-authorship network connecting the top 25 collaborators of Mikhail Ivanchenko. A scholar is included among the top collaborators of Mikhail Ivanchenko based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mikhail Ivanchenko. Mikhail Ivanchenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sabbatinelli, Jacopo, Anna Rita Bonfigli, Angelica Giuliani, et al.. (2025). Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus. Frontiers in Endocrinology. 16. 1689312–1689312.
2.
Turubanova, Victoria D., et al.. (2025). Explainable Machine Learning Models for Glioma Subtype Classification and Survival Prediction. Cancers. 17(16). 2614–2614.
3.
4.
Kalyakulina, Alena, Igor Yusipov, Elena Kondakova, et al.. (2024). Inflammaging Markers in the Extremely Cold Climate: A Case Study of Yakutian Population. International Journal of Molecular Sciences. 25(24). 13741–13741. 4 indexed citations
5.
Yusipov, Igor, et al.. (2024). Map of epigenetic age acceleration: A worldwide analysis. Ageing Research Reviews. 100. 102418–102418. 12 indexed citations
6.
Kalyakulina, Alena, Igor Yusipov, Elena Kondakova, et al.. (2023). Small immunological clocks identified by deep learning and gradient boosting. Frontiers in Immunology. 14. 1177611–1177611. 11 indexed citations
7.
Ivanchenko, Mikhail, et al.. (2023). Metastable oscillations in an evolutionary game: Synchronization and control. Physics Letters A. 491. 129210–129210. 2 indexed citations
8.
Kalyakulina, Alena, Igor Yusipov, Alexey Moskalev, Claudio Franceschi, & Mikhail Ivanchenko. (2023). eXplainable Artificial Intelligence (XAI) in aging clock models. Ageing Research Reviews. 93. 102144–102144. 23 indexed citations
9.
Krysko, Olga, Andrea Teufelberger, Natalie De Ruyck, et al.. (2023). Differential protease content of mast cells and the processing of IL-33 in Alternaria alternata induced allergic airway inflammation in mice. Frontiers in Immunology. 14. 1040493–1040493. 3 indexed citations
10.
Kalyakulina, Alena, Igor Yusipov, Maria Giulia Bacalini, et al.. (2022). Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI. GigaScience. 11. 12 indexed citations
11.
Krivonosov, Mikhail, et al.. (2022). A new cognitive clock matching phenotypic and epigenetic ages. Translational Psychiatry. 12(1). 364–364. 5 indexed citations
12.
Bacalini, Maria Giulia, et al.. (2021). Disentangling age-dependent DNA methylation: deterministic, stochastic, and nonlinear. Scientific Reports. 11(1). 9201–9201. 20 indexed citations
13.
Meyerov, Iosif, et al.. (2020). Transforming Lindblad Equations Into Systems of Real-Valued Linear Equations: Performance Optimization and Parallelization of an Algorithm. Duo Research Archive (University of Oslo). 1 indexed citations
14.
Meyerov, Iosif, et al.. (2017). Computation of the asymptotic states of modulated open quantum systems with a numerically exact realization of the quantum trajectory method. Physical review. E. 96(5). 53313–53313. 6 indexed citations
15.
Yusipov, Igor, T. V. Laptyeva, S. Denisov, & Mikhail Ivanchenko. (2017). Localization in Open Quantum Systems. Physical Review Letters. 118(7). 70402–70402. 27 indexed citations
16.
Laptyeva, T. V., et al.. (2015). Anderson attractors in active arrays. Scientific Reports. 5(1). 13263–13263. 9 indexed citations
17.
Boccaletti, Stefano, Mikhail Ivanchenko, Vito Latora, Alessandro Pluchino, & Andrea Rapisarda. (2007). Detecting complex network modularity by dynamical clustering. Physical Review E. 75(4). 45102–45102. 151 indexed citations
18.
Ivanchenko, Mikhail, et al.. (2007). Nearest-neighbor non-additivity versus long-range non-additivity in TATA-box structure and its implications for TBP-binding mechanism. Nucleic Acids Research. 35(13). 4409–4419. 24 indexed citations
19.
Osipov, Grigory V., Mikhail Ivanchenko, Jürgen Kurths, & Bambi Hu. (2005). Synchronized chaotic intermittent and spiking behavior in coupled map chains. Physical Review E. 71(5). 56209–56209. 16 indexed citations
20.
Flach, Sergej, Mikhail Ivanchenko, & Oleg Kanakov. (2005). q-Breathers and the Fermi-Pasta-Ulam Problem. Physical Review Letters. 95(6). 64102–64102. 89 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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