Alan R. Lowe

2.1k total citations
29 papers, 1.4k citations indexed

About

Alan R. Lowe is a scholar working on Molecular Biology, Biophysics and Materials Chemistry. According to data from OpenAlex, Alan R. Lowe has authored 29 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 7 papers in Biophysics and 5 papers in Materials Chemistry. Recurrent topics in Alan R. Lowe's work include Cell Image Analysis Techniques (7 papers), Protein Structure and Dynamics (6 papers) and Single-cell and spatial transcriptomics (5 papers). Alan R. Lowe is often cited by papers focused on Cell Image Analysis Techniques (7 papers), Protein Structure and Dynamics (6 papers) and Single-cell and spatial transcriptomics (5 papers). Alan R. Lowe collaborates with scholars based in United Kingdom, United States and Maldives. Alan R. Lowe's co-authors include Laura S. Itzhaki, Karsten Weis, Jan Liphardt, Christopher R. Lowe, Thomas R. Peskett, Frédérique Rau, Rickie Patani, Helen R. Saibil, Jonathan O’Driscoll and Guillaume Charras and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Alan R. Lowe

29 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alan R. Lowe United Kingdom 17 1.2k 241 163 110 109 29 1.4k
Vibor Laketa Germany 25 883 0.8× 284 1.2× 92 0.6× 98 0.9× 145 1.3× 41 1.6k
Felipe Merino Germany 23 1.4k 1.2× 440 1.8× 218 1.3× 142 1.3× 154 1.4× 32 2.2k
Pradeep Kota United States 18 934 0.8× 153 0.6× 125 0.8× 154 1.4× 55 0.5× 29 1.6k
Markus Stabrin Germany 8 764 0.7× 140 0.6× 105 0.6× 65 0.6× 58 0.5× 10 1.2k
Thomas J. Magliery United States 22 1.7k 1.5× 189 0.8× 255 1.6× 58 0.5× 127 1.2× 51 2.1k
Grigory Sharov United Kingdom 8 722 0.6× 159 0.7× 106 0.7× 59 0.5× 53 0.5× 11 1.1k
Lisa D. Cabrita United Kingdom 28 1.6k 1.4× 220 0.9× 325 2.0× 125 1.1× 46 0.4× 57 2.1k
Carine van Heijenoort France 18 586 0.5× 308 1.3× 114 0.7× 58 0.5× 95 0.9× 47 993
Alois Sonnleitner Austria 20 1.0k 0.9× 205 0.9× 50 0.3× 156 1.4× 151 1.4× 35 1.5k
Rosemary Williams United States 14 2.4k 2.1× 405 1.7× 241 1.5× 35 0.3× 67 0.6× 18 2.7k

Countries citing papers authored by Alan R. Lowe

Since Specialization
Citations

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

Fields of papers citing papers by Alan R. Lowe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alan R. Lowe. 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 Alan R. Lowe. The network helps show where Alan R. Lowe may publish in the future.

Co-authorship network of co-authors of Alan R. Lowe

This figure shows the co-authorship network connecting the top 25 collaborators of Alan R. Lowe. A scholar is included among the top collaborators of Alan R. Lowe 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 Alan R. Lowe. Alan R. Lowe 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.
Shannon, Michael J., et al.. (2024). cellPLATO – an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data. Journal of Cell Science. 137(20). 8 indexed citations
2.
Lowe, Alan R., et al.. (2024). Discovering interpretable models of scientific image data with deep learning. 6884–6893. 3 indexed citations
3.
Lowe, Alan R., et al.. (2023). Machine learning enhanced cell tracking. SHILAP Revista de lepidopterología. 3. 1228989–1228989. 4 indexed citations
4.
Day, Nathan, et al.. (2022). Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging. Methods in molecular biology. 2476. 17–30. 1 indexed citations
5.
Vallardi, Giulia, et al.. (2021). Automated Deep Lineage Tree Analysis Using a Bayesian Single Cell Tracking Approach. Frontiers in Computer Science. 3. 43 indexed citations
6.
Modi, Souvik, Guillermo López‐Doménech, Els F. Halff, et al.. (2019). Miro clusters regulate ER-mitochondria contact sites and link cristae organization to the mitochondrial transport machinery. Nature Communications. 10(1). 4399–4399. 138 indexed citations
7.
Lowe, Alan R., Albert Perez‐Riba, Laura S. Itzhaki, & Ewan R.G. Main. (2018). PyFolding: Open-Source Graphing, Simulation, and Analysis of the Biophysical Properties of Proteins. Biophysical Journal. 114(3). 516–521. 7 indexed citations
8.
Perez‐Riba, Albert, Alan R. Lowe, Ewan R.G. Main, & Laura S. Itzhaki. (2018). Context-Dependent Energetics of Loop Extensions in a Family of Tandem-Repeat Proteins. Biophysical Journal. 114(11). 2552–2562. 8 indexed citations
9.
Peskett, Thomas R., Frédérique Rau, Jonathan O’Driscoll, et al.. (2018). A Liquid to Solid Phase Transition Underlying Pathological Huntingtin Exon1 Aggregation. Molecular Cell. 70(4). 588–601.e6. 231 indexed citations
10.
Lowe, Alan R., Giorgio Saladino, Tom D. Bunney, et al.. (2017). Conformational transition of FGFR kinase activation revealed by site-specific unnatural amino acid reporter and single molecule FRET. Scientific Reports. 7(1). 39841–39841. 7 indexed citations
11.
Lowe, Alan R., Jeffrey H. Tang, Michael Graf, et al.. (2015). Importin-β modulates the permeability of the nuclear pore complex in a Ran-dependent manner. eLife. 4. 99 indexed citations
12.
Tang, Jeffrey H., et al.. (2013). Importin-Beta and Ran Regulate the Passive Permeability Barrier in the Nuclear Pore Complex. Biophysical Journal. 104(2). 120a–120a. 1 indexed citations
13.
Itzhaki, Laura S. & Alan R. Lowe. (2012). From Artificial Antibodies to Nanosprings. Advances in experimental medicine and biology. 747. 153–166. 7 indexed citations
14.
Lowe, Alan R., Jake J. Siegel, Petr Kaláb, et al.. (2010). Selectivity Mechanism of the Nuclear Pore Complex Characterized by Single Cargo Tracking. Biophysical Journal. 98(3). 209a–209a. 46 indexed citations
15.
Lowe, Alan R., Jake J. Siegel, Petr Kaláb, et al.. (2010). Selectivity mechanism of the nuclear pore complex characterized by single cargo tracking. Nature. 467(7315). 600–603. 128 indexed citations
16.
McGeoch, Adam, et al.. (2007). ATPase Site Architecture and Helicase Mechanism of an Archaeal MCM. Molecular Cell. 28(2). 304–314. 94 indexed citations
17.
Lao‐Sirieix, Pierre, Andrej Ćorović, Janusz Jankowski, et al.. (2007). Physiological and molecular analysis of acid loading mechanisms in squamous and columnar-lined esophagus. Diseases of the Esophagus. 21(6). 529–538. 16 indexed citations
18.
Lowe, Alan R. & Laura S. Itzhaki. (2006). Biophysical Characterisation of the Small Ankyrin Repeat Protein Myotrophin. Journal of Molecular Biology. 365(4). 1245–1255. 27 indexed citations
19.
Main, Ewan R.G., Alan R. Lowe, S. G. J. Mochrie, Stephen P. Jackson, & Lynne Regan. (2005). A recurring theme in protein engineering: the design, stability and folding of repeat proteins. Current Opinion in Structural Biology. 15(4). 464–471. 105 indexed citations
20.
Sheu, Laura, et al.. (1996). The vesicle-associated membrane protein family of proteins in rat pancreatic and parotid acinar cells. Gastroenterology. 111(6). 1661–1669. 47 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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