Matt Thomson

1.8k total citations
30 papers, 998 citations indexed

About

Matt Thomson is a scholar working on Molecular Biology, Biomedical Engineering and Oncology. According to data from OpenAlex, Matt Thomson has authored 30 papers receiving a total of 998 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 6 papers in Biomedical Engineering and 3 papers in Oncology. Recurrent topics in Matt Thomson's work include Single-cell and spatial transcriptomics (6 papers), Gene Regulatory Network Analysis (5 papers) and Pluripotent Stem Cells Research (4 papers). Matt Thomson is often cited by papers focused on Single-cell and spatial transcriptomics (6 papers), Gene Regulatory Network Analysis (5 papers) and Pluripotent Stem Cells Research (4 papers). Matt Thomson collaborates with scholars based in United States, United Kingdom and Germany. Matt Thomson's co-authors include Zack Smith, Sharad Ramanathan, Siyuan Liu, Alexander Meissner, Ling-Nan Zou, Graham Heimberg, Hana El‐Samad, Rajat Bhatnagar, Jake Cornwall-Scoones and David A. Sivak and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Matt Thomson

30 papers receiving 991 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Matt Thomson United States	14	793	106	84	71	61	30	998
Helene Kretzmer Germany	14	1.2k 1.5×	100 0.9×	184 2.2×	61 0.9×	68 1.1×	29	1.3k
Stavroula Skylaki Switzerland	11	608 0.8×	122 1.2×	49 0.6×	46 0.6×	39 0.6×	17	797
Ivana Barbaric United Kingdom	19	881 1.1×	147 1.4×	184 2.2×	106 1.5×	102 1.7×	41	1.1k
Nadine Schrode United States	14	827 1.0×	80 0.8×	215 2.6×	87 1.2×	93 1.5×	24	986
Néstor Saiz United States	15	1.1k 1.4×	122 1.2×	100 1.2×	109 1.5×	101 1.7×	22	1.2k
Xuan Zheng China	15	607 0.8×	172 1.6×	131 1.6×	42 0.6×	72 1.2×	34	1.0k
Evan Bardot United States	9	487 0.6×	85 0.8×	29 0.3×	43 0.6×	104 1.7×	11	678
Álvaro Plaza Reyes Sweden	11	1.0k 1.3×	77 0.7×	205 2.4×	31 0.4×	41 0.7×	16	1.2k
Andrei L. Turinsky Canada	18	710 0.9×	128 1.2×	198 2.4×	54 0.8×	105 1.7×	42	1.2k
Miroslav Hejna United States	10	465 0.6×	45 0.4×	68 0.8×	90 1.3×	27 0.4×	11	634

Countries citing papers authored by Matt Thomson

Since Specialization

Citations

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

Fields of papers citing papers by Matt Thomson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matt Thomson

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Xu, Alexander M., et al.. (2025). Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles. Nature Biomedical Engineering. 9(3). 390–404. 4 indexed citations

Yang, Fan, Shichen Liu, Heun Jin Lee, Rob Phillips, & Matt Thomson. (2025). Dynamic flow control through active matter programming language. Nature Materials. 24(4). 615–625. 4 indexed citations

Thomson, Matt, et al.. (2024). Automated construction of cognitive maps with visual predictive coding. Nature Machine Intelligence. 6(7). 820–833. 5 indexed citations

Swedlund, Benjamin, et al.. (2024). Control of spatio-temporal patterning via cell growth in a multicellular synthetic gene circuit. Nature Communications. 15(1). 9867–9867. 4 indexed citations

Polonsky, Michal, Louisa M.S. Gerhardt, Jina Yun, et al.. (2024). Spatial transcriptomics defines injury specific microenvironments and cellular interactions in kidney regeneration and disease. Nature Communications. 15(1). 7010–7010. 25 indexed citations

Thomson, Matt, et al.. (2024). Engineering flexible machine learning systems by traversing functionally invariant paths. Nature Machine Intelligence. 6(10). 1179–1196. 5 indexed citations

Huycke, Tyler R., Teemu J. Häkkinen, Vasudha Srivastava, et al.. (2024). Patterning and folding of intestinal villi by active mesenchymal dewetting. Cell. 187(12). 3072–3089.e20. 32 indexed citations

Winnett, Alexander Viloria, Reid Akana, Saharai Caldera, et al.. (2023). Extreme differences in SARS-CoV-2 viral loads among respiratory specimen types during presumed pre-infectious and infectious periods. PNAS Nexus. 2(3). 13 indexed citations

Pool, Allan‐Hermann, et al.. (2023). Recovery of missing single-cell RNA-sequencing data with optimized transcriptomic references. Nature Methods. 20(10). 1506–1515. 19 indexed citations

10.

Thomson, Matt. (2023). Spin glasses, error correcting codes, and synchronization of human stem cell organoids. Cell. 186(3). 461–463. 1 indexed citations

11.

Bao, Min, Jake Cornwall-Scoones, Andy Cox, et al.. (2022). Stem cell-derived synthetic embryos self-assemble by exploiting cadherin codes and cortical tension. Nature Cell Biology. 24(9). 1341–1349. 46 indexed citations

12.

Chen, Xiaoqiao, Sisi Chen, & Matt Thomson. (2022). Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM. Nature Computational Science. 2(6). 387–398. 7 indexed citations

13.

Thomson, Matt, et al.. (2022). Localization of signaling receptors maximizes cellular information acquisition in spatially structured natural environments. Cell Systems. 13(7). 530–546.e12. 5 indexed citations

14.

Chen, Xinyue, Sonali Chaturvedi, Weihan Li, et al.. (2021). A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions. Science. 373(6557). 60 indexed citations

15.

Chen, Sisi, Jong‐Hwan Park, Emeric Charles, et al.. (2020). Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign. Proceedings of the National Academy of Sciences. 117(46). 28784–28794. 19 indexed citations

16.

Zhu, Meng, Jake Cornwall-Scoones, Peizhe Wang, et al.. (2020). Developmental clock and mechanism of de novo polarization of the mouse embryo. Science. 370(6522). 67 indexed citations

17.

Heimberg, Graham, Rajat Bhatnagar, Hana El‐Samad, & Matt Thomson. (2016). Low Dimensionality in Gene Expression Data Enables the Accurate Extraction of Transcriptional Programs from Shallow Sequencing. Cell Systems. 2(4). 239–250. 95 indexed citations

18.

Liu, Yanxia, et al.. (2015). Transcription Factor Competition Allows Embryonic Stem Cells to Distinguish Authentic Signals from Noise. Cell Systems. 1(2). 117–129. 60 indexed citations

19.

Sivak, David A. & Matt Thomson. (2015). Environmental Statistics and Optimal Regulation. Biophysical Journal. 108(2). 364a–365a. 1 indexed citations

20.

Thomson, Matt, Siyuan Liu, Ling-Nan Zou, et al.. (2011). Pluripotency Factors in Embryonic Stem Cells Regulate Differentiation into Germ Layers. Cell. 145(6). 875–889. 403 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.

Explore authors with similar magnitude of impact