Subhajyoti De

7.3k total citations
89 papers, 3.8k citations indexed

About

Subhajyoti De is a scholar working on Molecular Biology, Cancer Research and Genetics. According to data from OpenAlex, Subhajyoti De has authored 89 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Molecular Biology, 45 papers in Cancer Research and 23 papers in Genetics. Recurrent topics in Subhajyoti De's work include Cancer Genomics and Diagnostics (39 papers), DNA Repair Mechanisms (15 papers) and Epigenetics and DNA Methylation (13 papers). Subhajyoti De is often cited by papers focused on Cancer Genomics and Diagnostics (39 papers), DNA Repair Mechanisms (15 papers) and Epigenetics and DNA Methylation (13 papers). Subhajyoti De collaborates with scholars based in United States, United Kingdom and Canada. Subhajyoti De's co-authors include Franziska Michor, Sarah A. Teichmann, Vinod Kumar Yadav, Emmanuel D. Levy, Antara Biswas, Markus Riester, Ondřej Podlaha, Anchal Sharma, Marie Guillet and David Pellman and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Subhajyoti De

84 papers receiving 3.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Subhajyoti De United States 33 2.6k 1.1k 712 676 315 89 3.8k
Raj Chari United States 36 3.2k 1.2× 816 0.8× 541 0.8× 603 0.9× 299 0.9× 98 4.2k
Nicola Crosetto Sweden 30 3.7k 1.4× 1.0k 0.9× 428 0.6× 1.0k 1.5× 310 1.0× 72 4.6k
Rory Stark United Kingdom 16 4.1k 1.5× 1.0k 1.0× 927 1.3× 597 0.9× 585 1.9× 21 5.3k
Tengfei Xiao China 27 3.5k 1.3× 945 0.9× 382 0.5× 678 1.0× 189 0.6× 68 4.6k
Nigel P. Mongan United Kingdom 39 3.7k 1.4× 1.3k 1.2× 627 0.9× 1.1k 1.6× 212 0.7× 158 5.7k
Xi Shi China 14 4.4k 1.7× 511 0.5× 685 1.0× 678 1.0× 186 0.6× 30 5.3k
Teemu Kivioja Finland 21 4.4k 1.7× 589 0.5× 684 1.0× 379 0.6× 484 1.5× 33 5.2k
Haijuan Yang United States 13 4.5k 1.7× 444 0.4× 674 0.9× 692 1.0× 221 0.7× 20 5.1k
Arttu Jolma Sweden 19 5.4k 2.0× 669 0.6× 823 1.2× 374 0.6× 524 1.7× 25 6.3k
Adrian P. Bracken Ireland 33 7.4k 2.8× 1.2k 1.1× 1.0k 1.4× 1.1k 1.7× 332 1.1× 47 8.5k

Countries citing papers authored by Subhajyoti De

Since Specialization
Citations

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

Fields of papers citing papers by Subhajyoti De

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Subhajyoti De

This figure shows the co-authorship network connecting the top 25 collaborators of Subhajyoti De. A scholar is included among the top collaborators of Subhajyoti De 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 Subhajyoti De. Subhajyoti De 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.
Li, Yingjie, Anjun Ma, W. Evan Johnson, et al.. (2025). The new microbiome on the block: challenges and opportunities of using human tumor sequencing data to study microbes. Nature Methods. 22(9). 1788–1799.
2.
Liu, Juan, Jie Liu, Dandan Xu, et al.. (2025). Lipogenic enzyme FASN promotes mutant p53 accumulation and gain-of-function through palmitoylation. Nature Communications. 16(1). 1762–1762. 3 indexed citations
3.
De, Subhajyoti, et al.. (2024). Cell-free nucleic acid fragmentomics: A non-invasive window into cellular epigenomes. Translational Oncology. 49. 102085–102085.
4.
Malhotra, Jyoti, et al.. (2024). Genomic and molecular alterations associated with primary resistance to immune checkpoint inhibitors. Cancer Immunology Immunotherapy. 73(11). 234–234. 3 indexed citations
5.
Hu, Xiaoju, Kuppusamy Balamurugan, Alexander Y. Mitrophanov, et al.. (2024). Mismatch repair protein MLH1 suppresses replicative stress in BRCA2-deficient breast tumors. Journal of Clinical Investigation. 134(7). 6 indexed citations
7.
Ghaddar, Bassel, Martin J. Blaser, & Subhajyoti De. (2023). Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues. Nature Computational Science. 3(9). 741–747. 8 indexed citations
8.
Ghaddar, Bassel & Subhajyoti De. (2022). Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq. Nucleic Acids Research. 50(14). e82–e82. 11 indexed citations
9.
De, Subhajyoti, et al.. (2020). Understanding the role of phenotypic switching in cancer drug resistance. Journal of Theoretical Biology. 490. 110162–110162. 41 indexed citations
10.
Thakar, Tanay, Nathanial J. Tolman, Anchal Sharma, et al.. (2020). Identification of regulators of poly-ADP-ribose polymerase inhibitor response through complementary CRISPR knockout and activation screens. Nature Communications. 11(1). 6118–6118. 35 indexed citations
11.
Liggett, L. Alexander, Anchal Sharma, Subhajyoti De, & James DeGregori. (2019). FERMI: A Novel Method for Sensitive Detection of Rare Mutations in Somatic Tissue. G3 Genes Genomes Genetics. 9(9). 2977–2987. 7 indexed citations
12.
Podell, Brendan K., et al.. (2018). Activation of S6 signaling is associated with cell survival and multinucleation in hyperplastic skin after epidermal loss of AURORA-A Kinase. Cell Death and Differentiation. 26(3). 548–564. 12 indexed citations
13.
Yamamoto, Kimiyo N., Shinichi Yachida, Akira Nakamura, et al.. (2017). Personalized Management of Pancreatic Ductal Adenocarcinoma Patients through Computational Modeling. Cancer Research. 77(12). 3325–3335. 9 indexed citations
14.
Rogozin, Igor B., Youri I. Pavlov, Alexander Goncearenco, et al.. (2017). Mutational signatures and mutable motifs in cancer genomes. Briefings in Bioinformatics. 19(6). 1085–1101. 39 indexed citations
15.
Nicolae, Claudia M., Daniel Constantin, Yuka Imamura Kawasawa, et al.. (2016). HUWE 1 interacts with PCNA to alleviate replication stress. EMBO Reports. 17(6). 874–886. 43 indexed citations
16.
Pei, Shanshan, Mohammad Minhajuddin, Angelo D’Alessandro, et al.. (2016). Rational Design of a Parthenolide-based Drug Regimen That Selectively Eradicates Acute Myelogenous Leukemia Stem Cells. Journal of Biological Chemistry. 291(42). 21984–22000. 33 indexed citations
17.
Nicolae, Claudia M., et al.. (2014). The ADP-ribosyltransferase PARP10/ARTD10 Interacts with Proliferating Cell Nuclear Antigen (PCNA) and Is Required for DNA Damage Tolerance. Journal of Biological Chemistry. 289(19). 13627–13637. 85 indexed citations
18.
Martins, Filipe Correia, Subhajyoti De, Vanessa Almendro, et al.. (2012). Evolutionary Pathways in BRCA1-Associated Breast Tumors. Cancer Discovery. 2(6). 503–511. 95 indexed citations
19.
De, Subhajyoti & Franziska Michor. (2011). DNA secondary structures and epigenetic determinants of cancer genome evolution. Nature Structural & Molecular Biology. 18(8). 950–955. 181 indexed citations
20.
De, Subhajyoti, Sarah A. Teichmann, & M. Madan Babu. (2009). The impact of genomic neighborhood on the evolution of human and chimpanzee transcriptome. Genome Research. 19(5). 785–794. 39 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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