Alexey Zaikin

4.9k total citations
146 papers, 3.2k citations indexed

About

Alexey Zaikin is a scholar working on Molecular Biology, Statistical and Nonlinear Physics and Computer Networks and Communications. According to data from OpenAlex, Alexey Zaikin has authored 146 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Molecular Biology, 50 papers in Statistical and Nonlinear Physics and 29 papers in Computer Networks and Communications. Recurrent topics in Alexey Zaikin's work include stochastic dynamics and bifurcation (42 papers), Nonlinear Dynamics and Pattern Formation (29 papers) and Gene Regulatory Network Analysis (27 papers). Alexey Zaikin is often cited by papers focused on stochastic dynamics and bifurcation (42 papers), Nonlinear Dynamics and Pattern Formation (29 papers) and Gene Regulatory Network Analysis (27 papers). Alexey Zaikin collaborates with scholars based in United Kingdom, Russia and Germany. Alexey Zaikin's co-authors include Jürgen Kurths, Jordi García‐Ojalvo, Ekkehard Ullner, Lutz Schimansky-Geier, P. S. Landa, Oleg Blyuss, Usha Menon, Arkady Pikovsky, E.I. Volkov and Evgenii Volkov and has published in prestigious journals such as Physical Review Letters, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Alexey Zaikin

136 papers receiving 3.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexey Zaikin United Kingdom 31 1.5k 1.2k 964 614 314 146 3.2k
Guang Hu China 34 1.8k 1.2× 2.1k 1.8× 1.4k 1.4× 648 1.1× 349 1.1× 268 4.4k
Hans G. Othmer United States 47 965 0.6× 1.2k 1.0× 4.0k 4.2× 217 0.4× 540 1.7× 139 8.2k
Jianwei Shuai China 36 754 0.5× 367 0.3× 2.3k 2.4× 523 0.9× 152 0.5× 196 4.3k
Lijian Yang China 28 1.3k 0.9× 688 0.6× 754 0.8× 1.1k 1.8× 651 2.1× 177 3.3k
Lai-Sang Young United States 32 2.4k 1.6× 650 0.6× 333 0.3× 393 0.6× 155 0.5× 95 4.3k
Chunni Wang China 42 3.3k 2.2× 2.0k 1.7× 393 0.4× 2.7k 4.4× 126 0.4× 132 4.8k
Ya Jia China 40 3.8k 2.6× 2.2k 1.9× 3.2k 3.3× 2.7k 4.4× 71 0.2× 210 7.5k
Alvin Shrier Canada 40 1.2k 0.8× 1.0k 0.9× 2.4k 2.5× 686 1.1× 52 0.2× 135 5.1k
Martin Falcke Germany 36 976 0.7× 709 0.6× 2.3k 2.4× 450 0.7× 50 0.2× 114 3.9k
Dibakar Ghosh India 42 3.7k 2.5× 4.1k 3.5× 406 0.4× 2.0k 3.2× 47 0.1× 257 6.3k

Countries citing papers authored by Alexey Zaikin

Since Specialization
Citations

This map shows the geographic impact of Alexey Zaikin'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 Alexey Zaikin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexey Zaikin more than expected).

Fields of papers citing papers by Alexey Zaikin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexey Zaikin. 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 Alexey Zaikin. The network helps show where Alexey Zaikin may publish in the future.

Co-authorship network of co-authors of Alexey Zaikin

This figure shows the co-authorship network connecting the top 25 collaborators of Alexey Zaikin. A scholar is included among the top collaborators of Alexey Zaikin 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 Alexey Zaikin. Alexey Zaikin 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.
2.
Gao, Yu, et al.. (2025). System-size coherence resonance in coupled FHN neurons with α -stable Lévy noise. Chaos Solitons & Fractals. 200. 116985–116985. 1 indexed citations
3.
Krivonosov, Mikhail, et al.. (2024). Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks. SHILAP Revista de lepidopterología. 13(1). 13–13.
4.
Ney, Alexander, Pilar Acedo, Oleg Blyuss, et al.. (2024). Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection. PLoS Computational Biology. 20(8). e1012408–e1012408.
5.
Suvorov, А. Yu., et al.. (2023). Basic aspects of meta-analysis. Part 1. SHILAP Revista de lepidopterología. 14(1). 4–14. 1 indexed citations
6.
Gordleeva, Susanna, Mikhail Krivonosov, Ivan Tyukin, et al.. (2023). Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network. IEEE Transactions on Neural Networks and Learning Systems. 36(1). 881–895. 8 indexed citations
7.
Yong, Hannah E. J., Esteban Salazar‐Petres, Tatiana Nazarenko, et al.. (2023). Integrated Placental Modelling of Histology with Gene Expression to Identify Functional Impact on Fetal Growth. Cells. 12(7). 1093–1093. 3 indexed citations
8.
Semyachkina‐Glushkovskaya, Oxana, Ivan V. Fedosov, Alexey Zaikin, et al.. (2023). Technology of the photobiostimulation of the brain’s drainage system during sleep for improvement of learning and memory in male mice. Biomedical Optics Express. 15(1). 44–44. 9 indexed citations
9.
Alsford, Sam, et al.. (2022). Synthetic biology tools for engineering Goodwin oscillation in Trypanosoma brucei brucei. Heliyon. 8(2). e08891–e08891. 1 indexed citations
10.
Gordleeva, Susanna, et al.. (2022). Impact of Astrocytic Coverage of Synapses on the Short-Term Memory of a Computational Neuron-Astrocyte Network. Mathematics. 10(18). 3275–3275. 1 indexed citations
11.
Zaikin, Alexey, et al.. (2021). Application of Machine Learning for Prediction of Cone Penetration Test Data. IOP Conference Series Earth and Environmental Science. 666(3). 32098–32098.
12.
Whitwell, Harry J., Jenny Worthington, Oleg Blyuss, et al.. (2020). Improved early detection of ovarian cancer using longitudinal multimarker models. British Journal of Cancer. 122(6). 847–856. 69 indexed citations
13.
Gentry‐Maharaj, Aleksandra, Oleg Blyuss, Andy Ryan, et al.. (2020). Multi-Marker Longitudinal Algorithms Incorporating HE4 and CA125 in Ovarian Cancer Screening of Postmenopausal Women. Cancers. 12(7). 1931–1931. 22 indexed citations
14.
Blyuss, Oleg, Alexey Zaikin, Daniel Munblit, et al.. (2019). Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients. British Journal of Cancer. 122(5). 692–696. 43 indexed citations
15.
Blyuss, Oleg, Matthew Burnell, Andy Ryan, et al.. (2018). Comparison of Longitudinal CA125 Algorithms as a First-Line Screen for Ovarian Cancer in the General Population. Clinical Cancer Research. 24(19). 4726–4733. 39 indexed citations
16.
Jalan, Sarika, et al.. (2016). Interplay of degree correlations and cluster synchronization. Physical review. E. 94(6). 62202–62202. 14 indexed citations
17.
Smith, Patrick M., et al.. (2016). Open source approaches to establishing Roseobacter clade bacteria as synthetic biology chassis for biogeoengineering. PeerJ. 4. e2031–e2031. 6 indexed citations
18.
O’Brien, Darragh P., Neomal S. Sandanayake, Claire Jenkinson, et al.. (2014). Serum CA19-9 Is Significantly Upregulated up to 2 Years before Diagnosis with Pancreatic Cancer: Implications for Early Disease Detection. Clinical Cancer Research. 21(3). 622–631. 147 indexed citations
19.
Alshaker, Heba, Jonathan Krell, Adam E. Frampton, et al.. (2014). Leptin induces upregulation of sphingosine kinase 1 in oestrogen receptor-negative breast cancer via Src family kinase-mediated, janus kinase 2-independent pathway. Breast Cancer Research. 16(5). 426–426. 42 indexed citations
20.
Zaikin, Alexey, et al.. (2010). Gene regulatory network attractor selection and cell fate decision: insights into cancer multi-targeting. UCL Discovery (University College London). 1 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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