Arjun S. Nanda

1.7k total citations · 1 hit paper
9 papers, 613 citations indexed

About

Arjun S. Nanda is a scholar working on Molecular Biology, Cancer Research and Cell Biology. According to data from OpenAlex, Arjun S. Nanda has authored 9 papers receiving a total of 613 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 5 papers in Cancer Research and 2 papers in Cell Biology. Recurrent topics in Arjun S. Nanda's work include CRISPR and Genetic Engineering (3 papers), Cancer Genomics and Diagnostics (3 papers) and Advanced biosensing and bioanalysis techniques (2 papers). Arjun S. Nanda is often cited by papers focused on CRISPR and Genetic Engineering (3 papers), Cancer Genomics and Diagnostics (3 papers) and Advanced biosensing and bioanalysis techniques (2 papers). Arjun S. Nanda collaborates with scholars based in United Kingdom, United States and Malaysia. Arjun S. Nanda's co-authors include Xueqing Zou, Serena Nik‐Zainal, Sandro Morganella, Andrea Degasperi, Volker M. Arlt, David H. Phillips, Eszter Nagy, Céline Gomez, Rebecca Harris and Jill E. Kucab and has published in prestigious journals such as Cell, Nature Genetics and Biochemical Journal.

In The Last Decade

Arjun S. Nanda

8 papers receiving 610 citations

Hit Papers

A Compendium of Mutational Signatures of Environmental Ag... 2019 2026 2021 2023 2019 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Arjun S. Nanda United Kingdom 7 339 336 133 100 83 9 613
Angela Hadjipanayis United States 11 369 1.1× 252 0.8× 156 1.2× 71 0.7× 39 0.5× 14 598
Tú Nguyen‐Dumont Australia 14 342 1.0× 172 0.5× 302 2.3× 111 1.1× 82 1.0× 52 647
Travis Drucker United States 8 194 0.6× 135 0.4× 123 0.9× 99 1.0× 45 0.5× 10 523
Yong Zhu China 16 446 1.3× 162 0.5× 68 0.5× 111 1.1× 60 0.7× 40 785
Wenlei Peng China 14 486 1.4× 508 1.5× 65 0.5× 145 1.4× 82 1.0× 19 815
Joy Nakitandwe United States 12 304 0.9× 101 0.3× 58 0.4× 69 0.7× 47 0.6× 30 521
Cláudia Bertan Brazil 17 310 0.9× 336 1.0× 171 1.3× 175 1.8× 94 1.1× 55 834
Mirna Jarosz United States 6 625 1.8× 272 0.8× 105 0.8× 105 1.1× 40 0.5× 10 903
Martin Lodén Netherlands 11 328 1.0× 117 0.3× 71 0.5× 190 1.9× 44 0.5× 14 627
Nasr Eldin Elwali Sudan 13 202 0.6× 193 0.6× 124 0.9× 163 1.6× 41 0.5× 24 657

Countries citing papers authored by Arjun S. Nanda

Since Specialization
Citations

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

Fields of papers citing papers by Arjun S. Nanda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arjun S. Nanda. 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 Arjun S. Nanda. The network helps show where Arjun S. Nanda may publish in the future.

Co-authorship network of co-authors of Arjun S. Nanda

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

All Works

9 of 9 papers shown
1.
Koh, Ching Chiek, Arjun S. Nanda, Andrea Degasperi, et al.. (2025). A redefined InDel taxonomy provides insights into mutational signatures. Nature Genetics. 57(5). 1132–1141.
2.
Nanda, Arjun S., Ke Wu, Iryna Irkliyenko, et al.. (2024). Direct transposition of native DNA for sensitive multimodal single-molecule sequencing. Nature Genetics. 56(6). 1300–1309. 8 indexed citations
3.
Culbertson, Bruce, Hosseinali Asgharian, Li Chen, et al.. (2023). A sense-antisense RNA interaction promotes breast cancer metastasis via regulation of NQO1 expression. Nature Cancer. 4(5). 682–698. 19 indexed citations
4.
Abdulhay, Nour J., Colin P McNally, Camille M. Moore, et al.. (2023). Nucleosome density shapes kilobase-scale regulation by a mammalian chromatin remodeler. Nature Structural & Molecular Biology. 30(10). 1571–1581. 17 indexed citations
5.
Degasperi, Andrea, Tauanne Dias Amarante, Jan Czarnecki, et al.. (2020). A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies. Nature Cancer. 1(2). 249–263. 127 indexed citations
6.
Kucab, Jill E., Xueqing Zou, Sandro Morganella, et al.. (2019). A Compendium of Mutational Signatures of Environmental Agents. Cell. 177(4). 821–836.e16. 358 indexed citations breakdown →
7.
Nanda, Arjun S., Christopher W. M. Kay, Elena Miranda, et al.. (2016). An antibody that prevents serpin polymerisation acts by inducing a novel allosteric behaviour. Biochemical Journal. 473(19). 3269–3290. 14 indexed citations
8.
Bolker, Benjamin M., et al.. (2009). Transient virulence of emerging pathogens. Journal of The Royal Society Interface. 7(46). 811–822. 66 indexed citations
9.
McLean, W.H. Irwin, Michèle Ramsay, G. C. ASHTON, et al.. (2002). Lipoid proteinosis maps to 1q21 and is caused by mutations in the extracellular matrix protein 1 gene. Journal of Investigative Dermatology. 119(1). 226–226. 4 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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