A. Middleton

1.8k total citations
33 papers, 818 citations indexed

About

A. Middleton is a scholar working on Molecular Biology, Computational Theory and Mathematics and Small Animals. According to data from OpenAlex, A. Middleton has authored 33 papers receiving a total of 818 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 6 papers in Small Animals. Recurrent topics in A. Middleton's work include Computational Drug Discovery Methods (8 papers), Animal testing and alternatives (6 papers) and Effects and risks of endocrine disrupting chemicals (4 papers). A. Middleton is often cited by papers focused on Computational Drug Discovery Methods (8 papers), Animal testing and alternatives (6 papers) and Effects and risks of endocrine disrupting chemicals (4 papers). A. Middleton collaborates with scholars based in United Kingdom, United States and Germany. A. Middleton's co-authors include Markus R. Owen, John R. King, Malcolm J. Bennett, Paul L. Carmichael, Susana Úbeda-Tomás, Jiabin Guo, Shuangqing Peng, Qiang Zhang, Jin Li and Christian Fleck and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Plant Cell and Cell Reports.

In The Last Decade

A. Middleton

27 papers receiving 806 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
A. Middleton United Kingdom 15 436 335 85 75 63 33 818
Edgar Weber Germany 16 211 0.5× 54 0.2× 31 0.4× 45 0.6× 26 0.4× 28 689
Pamela E. Blackshear United States 14 313 0.7× 32 0.1× 15 0.2× 72 1.0× 24 0.4× 21 673
Sreenivasa Ramaiahgari United States 13 361 0.8× 26 0.1× 12 0.1× 77 1.0× 67 1.1× 20 852
Simone Schmitz‐Spanke Germany 15 293 0.7× 36 0.1× 38 0.4× 153 2.0× 18 0.3× 43 768
Marie‐Pier Scott‐Boyer Canada 13 569 1.3× 45 0.1× 23 0.3× 27 0.4× 12 0.2× 42 918
Lin Bai China 13 378 0.9× 113 0.3× 21 0.2× 30 0.4× 27 0.4× 36 821
Marijana Radonjić Netherlands 14 594 1.4× 47 0.1× 28 0.3× 35 0.5× 8 0.1× 29 914
Patrick D. McMullen United States 17 310 0.7× 25 0.1× 9 0.1× 171 2.3× 98 1.6× 40 692
Anja Wilmes Austria 22 699 1.6× 57 0.2× 10 0.1× 113 1.5× 43 0.7× 45 1.3k
Takashi Yamoto Japan 16 329 0.8× 136 0.4× 5 0.1× 53 0.7× 15 0.2× 53 689

Countries citing papers authored by A. Middleton

Since Specialization
Citations

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

Fields of papers citing papers by A. Middleton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of A. Middleton

This figure shows the co-authorship network connecting the top 25 collaborators of A. Middleton. A scholar is included among the top collaborators of A. Middleton 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 A. Middleton. A. Middleton 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.
Ávila, Renato Ivan de, Iris Müller, Helen Barlow, et al.. (2025). Evaluation of a non-animal toolbox informed by adverse outcome pathways for human inhalation safety. Frontiers in Toxicology. 7. 1426132–1426132.
2.
Edwards, Christopher J., et al.. (2025). Evaluating the Accuracy of Large Language Models in Pharmaceutical Calculations: A Comparison of ChatGPT and MathGPT. American Journal of Pharmaceutical Education. 90(1). 101910–101910.
3.
Sharma, Raju Prasad, Bas ter Braak, Marije Niemeijer, et al.. (2025). Deciphering the quantitative relationship between NRF2 and SRXN1 through semi-mechanistic computational modeling. Toxicology. 519. 154284–154284.
4.
Maertens, Alexandra, Eric Bridgeford, Céline Brochot, et al.. (2025). From cellular perturbation to probabilistic risk assessments. ALTEX. 42(3). 413–434.
5.
Baltazar, Maria Teresa, Paul L. Carmichael, Matthew Dent, et al.. (2024). Advancing systemic toxicity risk assessment: Evaluation of a NAM-based toolbox approach. Toxicological Sciences. 204(1). 79–95. 6 indexed citations
6.
Basili, Danilo, et al.. (2024). Searching for LINCS to Stress: Using Text Mining to Automate Reference Chemical Curation. Chemical Research in Toxicology. 37(6). 878–893. 2 indexed citations
7.
Harrill, Joshua, Logan J. Everett, Derik E. Haggard, et al.. (2023). Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates. Toxicology. 501. 153694–153694. 15 indexed citations
8.
Braak, Bas ter, Steven Wink, Steven Hiemstra, et al.. (2022). Mapping the dynamics of Nrf2 antioxidant and NFκB inflammatory responses by soft electrophilic chemicals in human liver cells defines the transition from adaptive to adverse responses. Toxicology in Vitro. 84. 105419–105419. 3 indexed citations
9.
10.
Yin, Jian, Jiabin Guo, Qiang Zhang, et al.. (2018). Doxorubicin-induced mitophagy and mitochondrial damage is associated with dysregulation of the PINK1/parkin pathway. Toxicology in Vitro. 51. 1–10. 165 indexed citations
11.
Middleton, A., Cristina Dal Bosco, Phillip Chlap, et al.. (2018). Data-Driven Modeling of Intracellular Auxin Fluxes Indicates a Dominant Role of the ER in Controlling Nuclear Auxin Uptake. Cell Reports. 22(11). 3044–3057. 23 indexed citations
12.
Zhang, Qiang, Jin Li, A. Middleton, Sudin Bhattacharya, & Rory B. Conolly. (2018). Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling. Frontiers in Public Health. 6. 261–261. 63 indexed citations
13.
Zhang, Li, Jiabin Guo, Qiang Zhang, et al.. (2018). Flutamide Induces Hepatic Cell Death and Mitochondrial Dysfunction via Inhibition of Nrf2‐Mediated Heme Oxygenase‐1. Oxidative Medicine and Cellular Longevity. 2018(1). 8017073–8017073. 17 indexed citations
14.
Stichel, Damian, A. Middleton, Benedikt Müller, et al.. (2017). An individual-based model for collective cancer cell migration explains speed dynamics and phenotype variability in response to growth factors. npj Systems Biology and Applications. 3(1). 5–5. 20 indexed citations
15.
Brown, Laura E., A. Middleton, John R. King, & Matthew Loose. (2016). Multicellular Mathematical Modelling of Mesendoderm Formation in Amphibians. Bulletin of Mathematical Biology. 78(3). 436–467.
16.
Middleton, A., Christian Fleck, & Ramon Grima. (2014). A continuum approximation to an off-lattice individual-cell based model of cell migration and adhesion. Journal of Theoretical Biology. 359. 220–232. 46 indexed citations
17.
Band, Leah R., Susana Úbeda-Tomás, Rosemary Dyson, et al.. (2012). Growth-induced hormone dilution can explain the dynamics of plant root cell elongation. Proceedings of the National Academy of Sciences. 109(19). 7577–7582. 94 indexed citations
18.
Middleton, A., John R. King, & Matthew Loose. (2012). Wave pinning and spatial patterning in a mathematical model of Antivin/Lefty–Nodal signalling. Journal of Mathematical Biology. 67(6-7). 1393–1424. 6 indexed citations
19.
Middleton, A., John R. King, Malcolm J. Bennett, & Markus R. Owen. (2010). Mathematical Modelling of the Aux/IAA Negative Feedback Loop. Bulletin of Mathematical Biology. 72(6). 1383–1407. 51 indexed citations
20.
Middleton, A., John R. King, & Matthew Loose. (2009). Bistability in a model of mesoderm and anterior mesendoderm specification in Xenopus laevis. Journal of Theoretical Biology. 260(1). 41–55. 7 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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