Vladislav Vyshemirsky

1.4k total citations
30 papers, 959 citations indexed

About

Vladislav Vyshemirsky is a scholar working on Molecular Biology, Computational Theory and Mathematics and Modeling and Simulation. According to data from OpenAlex, Vladislav Vyshemirsky has authored 30 papers receiving a total of 959 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 7 papers in Modeling and Simulation. Recurrent topics in Vladislav Vyshemirsky's work include Gene Regulatory Network Analysis (10 papers), Mathematical Biology Tumor Growth (7 papers) and Computational Drug Discovery Methods (6 papers). Vladislav Vyshemirsky is often cited by papers focused on Gene Regulatory Network Analysis (10 papers), Mathematical Biology Tumor Growth (7 papers) and Computational Drug Discovery Methods (6 papers). Vladislav Vyshemirsky collaborates with scholars based in United Kingdom, Italy and Ireland. Vladislav Vyshemirsky's co-authors include Mark Girolami, David Gilbert, Richard Orton, Muffy Calder, Walter Kölch, Oliver Sturm, Olaf J. Rolinski, David J. S. Birch, Andrew R. Pitt and Boris Ν. Kholodenko and has published in prestigious journals such as Applied Physics Letters, Bioinformatics and The Journal of Physical Chemistry B.

In The Last Decade

Vladislav Vyshemirsky

29 papers receiving 940 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vladislav Vyshemirsky United Kingdom 13 668 145 94 69 63 30 959
Matthias König Germany 20 791 1.2× 84 0.6× 76 0.8× 46 0.7× 49 0.8× 51 1.4k
Vijayalakshmi Chelliah United Kingdom 17 1.2k 1.7× 227 1.6× 61 0.6× 35 0.5× 49 0.8× 22 1.5k
Yukiko Matsuoka Japan 13 1.6k 2.4× 243 1.7× 286 3.0× 107 1.6× 65 1.0× 20 2.2k
Jeremy L. Muhlich United States 19 1.1k 1.6× 402 2.8× 141 1.5× 41 0.6× 180 2.9× 25 1.4k
Dezső Módos United Kingdom 15 747 1.1× 126 0.9× 88 0.9× 53 0.8× 36 0.6× 37 1.1k
Xiaohong Jing United States 14 515 0.8× 101 0.7× 75 0.8× 20 0.3× 26 0.4× 24 898
Mehdi Sadeghi Iran 17 970 1.5× 137 0.9× 47 0.5× 34 0.5× 21 0.3× 104 1.3k
Attila Gábor Germany 14 615 0.9× 81 0.6× 83 0.9× 20 0.3× 68 1.1× 27 930
Céline Hernandez France 19 1.1k 1.7× 98 0.7× 75 0.8× 42 0.6× 27 0.4× 46 1.6k
Jorrit J. Hornberg Netherlands 18 812 1.2× 234 1.6× 147 1.6× 58 0.8× 69 1.1× 29 1.3k

Countries citing papers authored by Vladislav Vyshemirsky

Since Specialization
Citations

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

Fields of papers citing papers by Vladislav Vyshemirsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vladislav Vyshemirsky

This figure shows the co-authorship network connecting the top 25 collaborators of Vladislav Vyshemirsky. A scholar is included among the top collaborators of Vladislav Vyshemirsky 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 Vladislav Vyshemirsky. Vladislav Vyshemirsky 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.
Forbes, Shareen, et al.. (2022). Keratin intrinsic fluorescence as a mechanism for non-invasive monitoring of its glycation. Methods and Applications in Fluorescence. 11(1). 15003–15003. 5 indexed citations
3.
Birch, David J. S., et al.. (2022). Impact of the Flavonoid Quercetin on β-Amyloid Aggregation Revealed by Intrinsic Fluorescence. The Journal of Physical Chemistry B. 126(38). 7229–7237. 25 indexed citations
4.
Forbes, Shareen, et al.. (2021). Detecting beta-amyloid glycation by intrinsic fluorescence - Understanding the link between diabetes and Alzheimer's disease. Archives of Biochemistry and Biophysics. 704. 108886–108886. 12 indexed citations
5.
Forbes, Shareen, et al.. (2021). Collagen Glycation Detected by Its Intrinsic Fluorescence. The Journal of Physical Chemistry B. 125(39). 11058–11066. 18 indexed citations
6.
Birch, David J. S., et al.. (2019). Cu2+ Effects on Beta‐Amyloid Oligomerisation Monitored by the Fluorescence of Intrinsic Tyrosine. ChemPhysChem. 20(23). 3181–3185. 5 indexed citations
7.
Birch, David J. S., et al.. (2019). Protein fibrillogenesis model tracked by its intrinsic time-resolved emission spectra. Methods and Applications in Fluorescence. 7(3). 35003–35003. 2 indexed citations
8.
Birch, David J. S., et al.. (2019). Tracking Insulin Glycation in Real Time by Time-Resolved Emission Spectroscopy. The Journal of Physical Chemistry B. 123(37). 7812–7817. 3 indexed citations
9.
Alghamdi, Adel, Vladislav Vyshemirsky, David J. S. Birch, & Olaf J. Rolinski. (2017). Detecting beta-amyloid aggregation from time-resolved emission spectra. Methods and Applications in Fluorescence. 6(2). 24002–24002. 14 indexed citations
10.
Rolinski, Olaf J. & Vladislav Vyshemirsky. (2016). Fluorescence kinetics of tryptophan in a heterogeneous environment. Methods and Applications in Fluorescence. 4(1). 19501–19501.
11.
Rolinski, Olaf J., et al.. (2016). Resolving environmental microheterogeneity and dielectric relaxation in fluorescence kinetics of protein. Methods and Applications in Fluorescence. 4(2). 24001–24001. 7 indexed citations
12.
Milotti, E., et al.. (2014). Correction: Corrigendum: Interplay between distribution of live cells and growth dynamics of solid tumours. Scientific Reports. 4(1). 1 indexed citations
13.
Milotti, E., et al.. (2013). Computer-Aided Biophysical Modeling: A Quantitative Approach to Complex Biological Systems. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 10(3). 805–810. 2 indexed citations
14.
Chignola, Roberto, et al.. (2011). Modular model of TNFα cytotoxicity. Bioinformatics. 27(13). 1754–1757. 6 indexed citations
15.
Xu, Tian‐Rui, Vladislav Vyshemirsky, Amélie Gormand, et al.. (2010). Inferring Signaling Pathway Topologies from Multiple Perturbation Measurements of Specific Biochemical Species. Science Signaling. 3(113). ra20–ra20. 88 indexed citations
16.
Vyshemirsky, Vladislav & Mark Girolami. (2008). BioBayes: A software package for Bayesian inference in systems biology. Bioinformatics. 24(17). 1933–1934. 36 indexed citations
17.
Vyshemirsky, Vladislav & Mark Girolami. (2007). Bayesian ranking of biochemical system models. Bioinformatics. 24(6). 833–839. 130 indexed citations
18.
Vyshemirsky, Vladislav, Mark Girolami, Amélie Gormand, & Walter Kölch. (2006). A Bayesian Analysis of the ERK Signalling Pathway. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 3 indexed citations
19.
Calder, Muffy, Vladislav Vyshemirsky, David Gilbert, & Richard Orton. (2005). Analysis of signalling pathways using the prism model checker. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 36 indexed citations
20.
Orton, Richard, Oliver Sturm, Vladislav Vyshemirsky, et al.. (2005). Computational modelling of the receptor-tyrosine-kinase-activated MAPK pathway. Biochemical Journal. 392(2). 249–261. 251 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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