Neil Dalchau

3.7k total citations
42 papers, 2.5k citations indexed

About

Neil Dalchau is a scholar working on Molecular Biology, Plant Science and Genetics. According to data from OpenAlex, Neil Dalchau has authored 42 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 14 papers in Plant Science and 8 papers in Genetics. Recurrent topics in Neil Dalchau's work include Gene Regulatory Network Analysis (12 papers), Light effects on plants (8 papers) and Advanced biosensing and bioanalysis techniques (8 papers). Neil Dalchau is often cited by papers focused on Gene Regulatory Network Analysis (12 papers), Light effects on plants (8 papers) and Advanced biosensing and bioanalysis techniques (8 papers). Neil Dalchau collaborates with scholars based in United Kingdom, United States and Chile. Neil Dalchau's co-authors include Andrew Phillips, Georg Seelig, Alex Webb, Antony N. Dodd, Michael Gardner, Luca Cardelli, Richard A. Muscat, Katharine Hubbard, Yuan-Jyue Chen and Carlos Takeshi Hotta and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Neil Dalchau

41 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Neil Dalchau United Kingdom 23 1.7k 864 364 136 131 42 2.5k
Marten Postma Netherlands 26 1.5k 0.9× 446 0.5× 291 0.8× 101 0.7× 457 3.5× 61 2.9k
Amy S. Gladfelter United States 42 5.2k 3.0× 784 0.9× 383 1.1× 166 1.2× 305 2.3× 114 6.4k
Justin S. Bois United States 15 2.0k 1.2× 159 0.2× 601 1.7× 34 0.3× 135 1.0× 24 3.1k
Maria Israelsson Sweden 9 1.5k 0.9× 1.1k 1.3× 104 0.3× 54 0.4× 109 0.8× 10 2.2k
William O. Hancock United States 35 2.3k 1.4× 348 0.4× 390 1.1× 25 0.2× 206 1.6× 114 4.2k
Zhirong Bao United States 29 2.4k 1.4× 1.2k 1.4× 527 1.4× 60 0.4× 149 1.1× 67 4.4k
Arthur Prindle United States 18 1.6k 1.0× 363 0.4× 894 2.5× 59 0.4× 359 2.7× 24 2.8k
Gürol M. Süel United States 25 3.1k 1.8× 495 0.6× 513 1.4× 70 0.5× 464 3.5× 48 4.2k
Christian Fleck Germany 24 1.8k 1.1× 1.5k 1.8× 163 0.4× 58 0.4× 63 0.5× 62 2.5k
Christopher D. Wood Mexico 23 1.1k 0.7× 216 0.3× 257 0.7× 96 0.7× 346 2.6× 51 2.4k

Countries citing papers authored by Neil Dalchau

Since Specialization
Citations

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

Fields of papers citing papers by Neil Dalchau

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neil Dalchau

This figure shows the co-authorship network connecting the top 25 collaborators of Neil Dalchau. A scholar is included among the top collaborators of Neil Dalchau 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 Neil Dalchau. Neil Dalchau 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.
Dalchau, Neil, Megan E. Griffiths, Beatriz Jorrín, et al.. (2021). Conditional sanctioning in a legume– Rhizobium mutualism. Proceedings of the National Academy of Sciences. 118(19). 62 indexed citations
2.
Zhang, Jinny Xuemeng, Boyan Yordanov, Alexander L. Gaunt, et al.. (2021). A deep learning model for predicting next-generation sequencing depth from DNA sequence. Nature Communications. 12(1). 4387–4387. 47 indexed citations
3.
Guzmán‐Chávez, Fernando, Chiara Gandini, Tamara Matúte, et al.. (2021). Decentralizing Cell-Free RNA Sensing With the Use of Low-Cost Cell Extracts. Frontiers in Bioengineering and Biotechnology. 9. 727584–727584. 27 indexed citations
4.
Murphy, Niall, et al.. (2020). Stochastic pulsing of gene expression enables the generation of spatial patterns in Bacillus subtilis biofilms. Nature Communications. 11(1). 950–950. 26 indexed citations
5.
Grant, Paul K., Jacob Halatek, Attila Csikász‐Nagy, et al.. (2020). Interpretation of morphogen gradients by a synthetic bistable circuit. Nature Communications. 11(1). 5545–5545. 16 indexed citations
6.
Joesaar, Alex, Shuo Yang, Bas W. A. Bögels, et al.. (2019). DNA-based communication in populations of synthetic protocells. Nature Nanotechnology. 14(4). 369–378. 265 indexed citations
7.
Dalchau, Neil, et al.. (2018). Scaling up genetic circuit design for cellular computing: advances and prospects. Natural Computing. 17(4). 833–853. 48 indexed citations
8.
Boulanger, D., et al.. (2018). A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules. Frontiers in Immunology. 9. 1538–1538. 21 indexed citations
9.
Coveney, Peter V., et al.. (2017). Host genotype and time dependent antigen presentation of viral peptides: predictions from theory. Scientific Reports. 7(1). 14367–14367. 4 indexed citations
10.
Wan, Shunzhou, et al.. (2017). The Role of Multiscale Protein Dynamics in Antigen Presentation and T Lymphocyte Recognition. Frontiers in Immunology. 8. 797–797. 6 indexed citations
11.
Zhang, Jinny Xuemeng, John Fang, Lucia R. Wu, et al.. (2017). Predicting DNA hybridization kinetics from sequence. Nature Chemistry. 10(1). 91–98. 124 indexed citations
12.
Cardelli, Luca, et al.. (2017). Efficient Switches in Biology and Computer Science. PLoS Computational Biology. 13(1). e1005100–e1005100. 10 indexed citations
13.
Dalchau, Neil, et al.. (2017). A spatially localized architecture for fast and modular DNA computing. Nature Nanotechnology. 12(9). 920–927. 273 indexed citations
14.
Cardelli, Luca, Attila Csikász‐Nagy, Neil Dalchau, Mirco Tribastone, & Max Tschaikowski. (2016). Noise Reduction in Complex Biological Switches. Scientific Reports. 6(1). 20214–20214. 20 indexed citations
15.
Dalchau, Neil. (2011). Understanding biological timing using mechanistic and black‐box models. New Phytologist. 193(4). 852–858. 17 indexed citations
16.
Dalchau, Neil, Andrew Phillips, Leonard D. Goldstein, et al.. (2011). A Peptide Filtering Relation Quantifies MHC Class I Peptide Optimization. PLoS Computational Biology. 7(10). e1002144–e1002144. 68 indexed citations
17.
Lewsey, Mathew G., Alex M. Murphy, Dan MacLean, et al.. (2010). Disruption of Two Defensive Signaling Pathways by a Viral RNA Silencing Suppressor. Molecular Plant-Microbe Interactions. 23(7). 835–845. 142 indexed citations
18.
Hateren, Andy van, Edward James, A.G. Bailey, et al.. (2010). The cell biology of major histocompatibility complex class I assembly: towards a molecular understanding. Tissue Antigens. 76(4). 259–275. 50 indexed citations
19.
Hubbard, Katharine, Fiona C. Robertson, Neil Dalchau, & Alex Webb. (2009). Systems analyses of circadian networks. Molecular BioSystems. 5(12). 1502–1511. 22 indexed citations
20.
Dodd, Antony N., Michael Gardner, Carlos Takeshi Hotta, et al.. (2007). The Arabidopsis Circadian Clock Incorporates a cADPR-Based Feedback Loop. Science. 318(5857). 1789–1792. 170 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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