David Schnoerr

833 total citations
9 papers, 162 citations indexed

About

David Schnoerr is a scholar working on Molecular Biology, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, David Schnoerr has authored 9 papers receiving a total of 162 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 2 papers in Computational Theory and Mathematics and 1 paper in Computer Networks and Communications. Recurrent topics in David Schnoerr's work include Gene Regulatory Network Analysis (8 papers), Single-cell and spatial transcriptomics (2 papers) and Diffusion and Search Dynamics (2 papers). David Schnoerr is often cited by papers focused on Gene Regulatory Network Analysis (8 papers), Single-cell and spatial transcriptomics (2 papers) and Diffusion and Search Dynamics (2 papers). David Schnoerr collaborates with scholars based in United Kingdom, Australia and Germany. David Schnoerr's co-authors include Michael P. H. Stumpf, Mark Isalan, Guido Sanguinetti, Ramon Grima, Sean T. Vittadello, Rowan D. Brackston, David F. Anderson, Botond Cseke, Michael E. Rule and Matthias H. Hennig and has published in prestigious journals such as Physical Review Letters, Nature Communications and The Journal of Chemical Physics.

In The Last Decade

David Schnoerr

9 papers receiving 158 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Schnoerr United Kingdom	6	100	48	36	32	17	9	162
Ganesh A. Viswanathan India	11	151 1.5×	81 1.7×	12 0.3×	47 1.5×	14 0.8×	29	289
Gerd Gruenert Germany	10	186 1.9×	46 1.0×	35 1.0×	55 1.7×	7 0.4×	12	307
Angelina Peñaranda Spain	9	144 1.4×	22 0.5×	15 0.4×	25 0.8×	27 1.6×	23	293
Stefan Hellander Sweden	10	234 2.3×	13 0.3×	54 1.5×	23 0.7×	28 1.6×	17	278
Fernando Antoneli Brazil	11	139 1.4×	88 1.8×	33 0.9×	11 0.3×	49 2.9×	36	266
Vera Calenbuhr Belgium	8	53 0.5×	55 1.1×	31 0.9×	57 1.8×	61 3.6×	13	222
Stephen Smith United Kingdom	10	185 1.9×	9 0.2×	46 1.3×	34 1.1×	34 2.0×	10	237
R. Thomas Belgium	3	161 1.6×	27 0.6×	24 0.7×	5 0.2×	29 1.7×	3	234
Henry H. Mattingly United States	8	123 1.2×	11 0.2×	41 1.1×	64 2.0×	32 1.9×	15	250
Edward J. Hancock Australia	8	188 1.9×	21 0.4×	51 1.4×	25 0.8×	18 1.1×	19	263

Countries citing papers authored by David Schnoerr

Since Specialization

Citations

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

Fields of papers citing papers by David Schnoerr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Schnoerr

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

All Works

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9 of 9 papers shown

1.

Schnoerr, David, et al.. (2021). The design principles of discrete turing patterning systems. Journal of Theoretical Biology. 531. 110901–110901. 7 indexed citations

2.

Vittadello, Sean T., et al.. (2021). Turing pattern design principles and their robustness. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 379(2213). 20200272–20200272. 29 indexed citations

3.

Anderson, David F., et al.. (2020). Time-dependent product-form Poisson distributions for reaction networks with higher order complexes. Journal of Mathematical Biology. 80(6). 1919–1951. 9 indexed citations

4.

Schnoerr, David, et al.. (2020). Exactly solvable models of stochastic gene expression. The Journal of Chemical Physics. 152(14). 144106–144106. 22 indexed citations

5.

Rule, Michael E., David Schnoerr, Matthias H. Hennig, & Guido Sanguinetti. (2019). Neural field models for latent state inference: Application to large-scale neuronal recordings. PLoS Computational Biology. 15(11). e1007442–e1007442. 5 indexed citations

6.

Schnoerr, David, et al.. (2019). A Comprehensive Network Atlas Reveals That Turing Patterns Are Common but Not Robust. Cell Systems. 9(3). 243–257.e4. 61 indexed citations

7.

Schnoerr, David, Botond Cseke, Ramon Grima, & Guido Sanguinetti. (2017). Efficient Low-Order Approximation of First-Passage Time Distributions. Physical Review Letters. 119(21). 210601–210601. 5 indexed citations

8.

Schnoerr, David, et al.. (2017). An alternative route to the system-size expansion. Journal of Physics A Mathematical and Theoretical. 50(39). 395003–395003. 2 indexed citations

9.

Schnoerr, David, Ramon Grima, & Guido Sanguinetti. (2016). Cox process representation and inference for stochastic reaction–diffusion processes. Nature Communications. 7(1). 11729–11729. 22 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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