Maya Mincheva

527 total citations
21 papers, 317 citations indexed

About

Maya Mincheva is a scholar working on Molecular Biology, Computer Networks and Communications and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Maya Mincheva has authored 21 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 7 papers in Computer Networks and Communications and 3 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Maya Mincheva's work include Gene Regulatory Network Analysis (18 papers), Protein Structure and Dynamics (10 papers) and Nonlinear Dynamics and Pattern Formation (7 papers). Maya Mincheva is often cited by papers focused on Gene Regulatory Network Analysis (18 papers), Protein Structure and Dynamics (10 papers) and Nonlinear Dynamics and Pattern Formation (7 papers). Maya Mincheva collaborates with scholars based in United States, Canada and Germany. Maya Mincheva's co-authors include Marc R. Roussel, Carsten Conradi, Gheorghe Crăciun, Elisenda Feliu, David Siegel, Carsten Wiuf, Santiago Schnell, Brian Ingalls, Matthew Hartley and Casian Pantea and has published in prestigious journals such as The Journal of Chemical Physics, Proceedings of the IEEE and PLoS Computational Biology.

In The Last Decade

Maya Mincheva

20 papers receiving 304 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Maya Mincheva United States	10	238	58	36	36	25	21	317
Ruoshi Yuan China	14	229 1.0×	25 0.4×	143 4.0×	24 0.7×	31 1.2×	31	414
R. Thomas Belgium	3	161 0.7×	27 0.5×	29 0.8×	17 0.5×	24 1.0×	3	234
Nicolas Kellershohn France	8	272 1.1×	36 0.6×	55 1.5×	17 0.5×	50 2.0×	20	386
El Houssine Snoussi Morocco	5	366 1.5×	36 0.6×	38 1.1×	37 1.0×	98 3.9×	5	414
Hartmut Schwetlick United Kingdom	10	67 0.3×	23 0.4×	54 1.5×	58 1.6×	15 0.6×	24	316
Tetsuya J. Kobayashi Japan	12	332 1.4×	38 0.7×	109 3.0×	19 0.5×	127 5.1×	38	417
J. P. Kernévez France	10	164 0.7×	83 1.4×	32 0.9×	14 0.4×	16 0.6×	21	314
Julio Aracena Chile	12	373 1.6×	49 0.8×	27 0.8×	105 2.9×	51 2.0×	24	439
Elisenda Feliu Denmark	16	515 2.2×	30 0.5×	41 1.1×	155 4.3×	76 3.0×	49	667
Michael S. Samoilov United States	10	544 2.3×	32 0.6×	109 3.0×	27 0.8×	158 6.3×	15	649

Countries citing papers authored by Maya Mincheva

Since Specialization

Citations

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

Fields of papers citing papers by Maya Mincheva

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maya Mincheva

This figure shows the co-authorship network connecting the top 25 collaborators of Maya Mincheva. A scholar is included among the top collaborators of Maya Mincheva 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 Maya Mincheva. Maya Mincheva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Conradi, Carsten & Maya Mincheva. (2024). In distributive phosphorylation catalytic constants enable non-trivial dynamics. Journal of Mathematical Biology. 89(2). 20–20.

Crăciun, Gheorghe, et al.. (2022). A Graph-Theoretic Condition for Delay Stability of Reaction Systems. SIAM Journal on Applied Dynamical Systems. 21(2). 1092–1118. 4 indexed citations

Conradi, Carsten, et al.. (2019). On the existence of Hopf bifurcations in the sequential and distributive double phosphorylation cycle. Mathematical Biosciences & Engineering. 17(1). 494–513. 7 indexed citations

Conradi, Carsten, Elisenda Feliu, Maya Mincheva, & Carsten Wiuf. (2017). Identifying parameter regions for multistationarity. PLoS Computational Biology. 13(10). e1005751–e1005751. 42 indexed citations

Ingalls, Brian, Maya Mincheva, & Marc R. Roussel. (2017). Parametric Sensitivity Analysis of Oscillatory Delay Systems with an Application to Gene Regulation. Bulletin of Mathematical Biology. 79(7). 1539–1563. 7 indexed citations

Kyurkchiev, Nikolay, Svetoslav Markov, & Maya Mincheva. (2017). Analysis of Biochemical Mechanisms using Mathematica with Applications. 10(1). 63–78. 1 indexed citations

Mincheva, Maya, et al.. (2016). Linear stability of delayed reaction–diffusion systems. Computers & Mathematics with Applications. 73(2). 226–232. 2 indexed citations

Conradi, Carsten & Maya Mincheva. (2015). Graph-theoretic analysis of multistationarity using degree theory. Mathematics and Computers in Simulation. 133. 76–90. 7 indexed citations

Hartley, Matthew, et al.. (2014). GraTeLPy: graph-theoretic linear stability analysis. BMC Systems Biology. 8(1). 22–22. 7 indexed citations

10.

Conradi, Carsten & Maya Mincheva. (2014). Catalytic constants enable the emergence of bistability in dual phosphorylation. Journal of The Royal Society Interface. 11(95). 20140158–20140158. 33 indexed citations

11.

Mincheva, Maya, et al.. (2013). Network representations and methods for the analysis of chemical and biochemical pathways. Molecular BioSystems. 9(9). 2189–2200. 19 indexed citations

12.

Mincheva, Maya & Gheorghe Crăciun. (2013). Graph-theoretic conditions for zero-eigenvalue Turing instability in general chemical reaction networks. Mathematical Biosciences & Engineering. 10(4). 1207–1226. 1 indexed citations

13.

Mincheva, Maya & Marc R. Roussel. (2012). Turing-Hopf instability in biochemical reaction networks arising from pairs of subnetworks. Mathematical Biosciences. 240(1). 1–11. 9 indexed citations

14.

Mincheva, Maya. (2011). Oscillations in Biochemical Reaction Networks Arising from Pairs of Subnetworks. Bulletin of Mathematical Biology. 73(10). 2277–2304. 13 indexed citations

15.

Mincheva, Maya. (2011). Oscillations in non-mass action kinetics models of biochemical reaction networks arising from pairs of subnetworks. Journal of Mathematical Chemistry. 50(5). 1111–1125. 5 indexed citations

16.

Mincheva, Maya & Gheorghe Crăciun. (2008). Multigraph Conditions for Multistability, Oscillations and Pattern Formation in Biochemical Reaction Networks. Proceedings of the IEEE. 96(8). 1281–1291. 26 indexed citations

17.

Mincheva, Maya & Marc R. Roussel. (2007). Graph-theoretic methods for the analysis of chemical and biochemical networks. I. Multistability and oscillations in ordinary differential equation models. Journal of Mathematical Biology. 55(1). 61–86. 64 indexed citations

18.

Mincheva, Maya & Marc R. Roussel. (2007). Graph-theoretic methods for the analysis of chemical and biochemical networks. II. Oscillations in networks with delays. Journal of Mathematical Biology. 55(1). 87–104. 26 indexed citations

19.

Mincheva, Maya & Marc R. Roussel. (2006). A graph-theoretic method for detecting potential Turing bifurcations. The Journal of Chemical Physics. 125(20). 204102–204102. 21 indexed citations

20.

Mincheva, Maya & David Siegel. (2003). Stability of mass action reaction–diffusion systems. Nonlinear Analysis. 56(8). 1105–1131. 12 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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