Jonathan R. Dry

4.0k total citations · 1 hit paper
34 papers, 1.4k citations indexed

About

Jonathan R. Dry is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Jonathan R. Dry has authored 34 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 11 papers in Cancer Research and 9 papers in Oncology. Recurrent topics in Jonathan R. Dry's work include Cancer Genomics and Diagnostics (11 papers), Melanoma and MAPK Pathways (5 papers) and Lung Cancer Treatments and Mutations (5 papers). Jonathan R. Dry is often cited by papers focused on Cancer Genomics and Diagnostics (11 papers), Melanoma and MAPK Pathways (5 papers) and Lung Cancer Treatments and Mutations (5 papers). Jonathan R. Dry collaborates with scholars based in United States, United Kingdom and Singapore. Jonathan R. Dry's co-authors include Robert McEwen, J. Carl Barrett, Zhongwu Lai, Brian Dougherty, Miika Ahdesmäki, Aleksandra Markovets, Oliver Hofmann, Brad Chapman, Paul D. Smith and Matthew J. Sale and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Nature reviews. Cancer.

In The Last Decade

Jonathan R. Dry

33 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

2016 497 citations Zhongwu Lai, Aleksandra Markovets et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jonathan R. Dry United States	19	818	474	473	230	228	34	1.4k
Panisa Pochanard United States	5	1.2k 1.5×	596 1.3×	486 1.0×	229 1.0×	236 1.0×	6	1.6k
Kenric Leung United Kingdom	3	1.2k 1.5×	451 1.0×	696 1.5×	273 1.2×	249 1.1×	5	1.8k
Jason R. Dobson United States	18	1.4k 1.7×	559 1.2×	567 1.2×	362 1.6×	162 0.7×	24	2.0k
Sam Thompson United Kingdom	4	897 1.1×	295 0.6×	525 1.1×	207 0.9×	165 0.7×	7	1.3k
Dawn A. D. Chasse United States	6	1.8k 2.2×	688 1.5×	540 1.1×	305 1.3×	183 0.8×	10	2.3k
Dmitriy Sonkin United States	16	845 1.0×	640 1.4×	450 1.0×	191 0.8×	132 0.6×	31	1.4k
Andrea H. Bild United States	22	1.2k 1.4×	704 1.5×	504 1.1×	278 1.2×	114 0.5×	56	1.9k
Ferran Muiños Spain	11	933 1.1×	338 0.7×	660 1.4×	303 1.3×	171 0.8×	18	1.5k
Oriol Pich United Kingdom	11	805 1.0×	268 0.6×	571 1.2×	197 0.9×	153 0.7×	16	1.3k
Nils Weinhold United States	12	810 1.0×	282 0.6×	384 0.8×	110 0.5×	192 0.8×	19	1.2k

Countries citing papers authored by Jonathan R. Dry

Since Specialization

Citations

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

Fields of papers citing papers by Jonathan R. Dry

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonathan R. Dry

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

All Works

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20 of 20 papers shown

Lai, Zhongwu, Ethan S. Sokol, Mingchao Xie, et al.. (2022). Landscape of homologous recombination deficiencies in solid tumours: analyses of two independent genomic datasets. BMC Cancer. 22(1). 13–13. 28 indexed citations

Dougherty, Bonnie V., Joshua S. K. Bell, Jackson Michuda, et al.. (2022). Validation of genomic and transcriptomic models of homologous recombination deficiency in a real-world pan-cancer cohort. BMC Cancer. 22(1). 587–587. 28 indexed citations

Criscione, Steven W., Matthew J. Martin, Derek B. Oien, et al.. (2022). The landscape of therapeutic vulnerabilities in EGFR inhibitor osimertinib drug tolerant persister cells. npj Precision Oncology. 6(1). 95–95. 22 indexed citations

Gogleva, Anna, Dimitris Polychronopoulos, Matthias Pfeifer, et al.. (2022). Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer. Nature Communications. 13(1). 1667–1667. 48 indexed citations

Fang, Chao, Dong Xu, Jing Su, Jonathan R. Dry, & Bolan Linghu. (2021). DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy. npj Digital Medicine. 4(1). 14–14. 25 indexed citations

Yang, Mi, Patricia Jaaks, Jonathan R. Dry, et al.. (2020). Stratification and prediction of drug synergy based on target functional similarity. npj Systems Biology and Applications. 6(1). 16–16. 41 indexed citations

Dry, Jonathan R., Frank Dondelinger, Andreas Bender, et al.. (2020). Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens. Patterns. 1(5). 100065–100065. 9 indexed citations

Sale, Matthew J., Kathryn Balmanno, Eiko Ozono, et al.. (2019). MEK1/2 inhibitor withdrawal reverses acquired resistance driven by BRAFV600E amplification whereas KRASG13D amplification promotes EMT-chemoresistance. Nature Communications. 10(1). 2030–2030. 41 indexed citations

Brant, Roz, Alan Sharpe, Jonathan R. Dry, et al.. (2016). Clinically Viable Gene Expression Assays with Potential for Predicting Benefit from MEK Inhibitors. Clinical Cancer Research. 23(6). 1471–1480. 17 indexed citations

10.

Delpuech, Oona, Claire Rooney, Lorraine Mooney, et al.. (2016). Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547. Molecular Cancer Therapeutics. 15(11). 2802–2813. 18 indexed citations

11.

Silverbush, Dana, Shaun Grosskurth, Dennis Wang, et al.. (2016). Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia. Cancer Research. 77(4). 827–838. 25 indexed citations

12.

Dry, Jonathan R., Mi Yang, & Julio Sáez-Rodríguez. (2016). Looking beyond the cancer cell for effective drug combinations. Genome Medicine. 8(1). 125–125. 26 indexed citations

13.

Carr, T. Hedley, Robert McEwen, Brian Dougherty, et al.. (2016). Defining actionable mutations for oncology therapeutic development. Nature reviews. Cancer. 16(5). 319–329. 77 indexed citations

14.

Lai, Zhongwu, Aleksandra Markovets, Miika Ahdesmäki, et al.. (2016). VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Research. 44(11). e108–e108. 497 indexed citations breakdown →

15.

Wappett, Mark, et al.. (2016). Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs. BMC Genomics. 17(1). 65–65. 15 indexed citations

16.

Zhu, Wei, Michael Kuziora, Todd Creasy, et al.. (2015). BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality using next generation sequencing data. Nucleic Acids Research. 44(4). e38–e38. 12 indexed citations

17.

Guinney, Justin, Charles Ferté, Jonathan R. Dry, et al.. (2013). Modeling RAS Phenotype in Colorectal Cancer Uncovers Novel Molecular Traits of RAS Dependency and Improves Prediction of Response to Targeted Agents in Patients. Clinical Cancer Research. 20(1). 265–272. 30 indexed citations

18.

Bradford, James, Matthew R. Farren, Steve Powell, et al.. (2013). RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib. PLoS ONE. 8(6). e66003–e66003. 24 indexed citations

19.

Marshall, Gayle, Zoë Howard, Jonathan R. Dry, et al.. (2011). Benefits of mTOR kinase targeting in oncology: pre-clinical evidence with AZD8055. Biochemical Society Transactions. 39(2). 456–459. 28 indexed citations

20.

Yang, Zheng Rong, et al.. (2003). Searching for discrimination rules in protease proteolytic cleavage activity using genetic programming with a min-max scoring function. Biosystems. 72(1-2). 159–176. 16 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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