David Fenyö

28.9k total citations · 2 hit papers
192 papers, 9.8k citations indexed

About

David Fenyö is a scholar working on Molecular Biology, Spectroscopy and Plant Science. According to data from OpenAlex, David Fenyö has authored 192 papers receiving a total of 9.8k indexed citations (citations by other indexed papers that have themselves been cited), including 127 papers in Molecular Biology, 69 papers in Spectroscopy and 18 papers in Plant Science. Recurrent topics in David Fenyö's work include Advanced Proteomics Techniques and Applications (51 papers), Mass Spectrometry Techniques and Applications (50 papers) and Metabolomics and Mass Spectrometry Studies (22 papers). David Fenyö is often cited by papers focused on Advanced Proteomics Techniques and Applications (51 papers), Mass Spectrometry Techniques and Applications (50 papers) and Metabolomics and Mass Spectrometry Studies (22 papers). David Fenyö collaborates with scholars based in United States, Sweden and Canada. David Fenyö's co-authors include Brian T. Chait, Ronald C. Beavis, Narges Razavian, Theodore Sakellaropoulos, Aristotelis Tsirigos, Nicolas Coudray, Matija Snuderl, Paolo Ocampo, André L. Moreira and Navneet Narula 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

David Fenyö

188 papers receiving 9.7k 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.

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

2018 1.7k citations Nicolas Coudray, Theodore Sakellaropoulos et al. profile →
The role of retrotransposable elements in ageing and age-associated diseases

2021 232 citations Paolo Mita, Wilson McKerrow et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Fenyö United States	50	5.7k	2.3k	1.4k	1.2k	847	192	9.8k
Roland L. Dunbrack United States	52	13.6k 2.4×	1.0k 0.5×	1.5k 1.0×	259 0.2×	1.2k 1.5×	159	16.7k
John D. Pfeifer United States	49	12.7k 2.2×	1.6k 0.7×	952 0.7×	295 0.2×	2.2k 2.6×	176	20.8k
G.T. Montelione United States	66	12.5k 2.2×	2.5k 1.1×	1.1k 0.8×	103 0.1×	775 0.9×	360	16.1k
Arthur M. Lesk United Kingdom	61	13.1k 2.3×	921 0.4×	3.9k 2.7×	202 0.2×	749 0.9×	192	17.1k
Steven E. Brenner United States	49	19.2k 3.4×	699 0.3×	464 0.3×	438 0.4×	659 0.8×	141	23.7k
Patrick Argos Germany	72	15.5k 2.7×	1.2k 0.5×	737 0.5×	282 0.2×	1.1k 1.3×	191	21.6k
Predrag Radivojac United States	45	8.7k 1.5×	1.1k 0.5×	211 0.1×	243 0.2×	438 0.5×	145	10.6k
C. Chothia United Kingdom	33	11.2k 2.0×	717 0.3×	1.1k 0.8×	286 0.2×	442 0.5×	44	13.5k
Bernd Bodenmiller Switzerland	49	9.3k 1.6×	2.3k 1.0×	340 0.2×	217 0.2×	2.5k 3.0×	111	13.5k
Stephen P. A. Fodor United States	37	10.3k 1.8×	777 0.3×	953 0.7×	163 0.1×	384 0.5×	53	13.4k

Countries citing papers authored by David Fenyö

Since Specialization

Citations

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

Fields of papers citing papers by David Fenyö

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Fenyö

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wu, EX, et al.. (2025). BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation. IEEE Transactions on Visualization and Computer Graphics. 32(1). 1208–1218.

Fridy, Peter C., Ryan Farrell, Kelly R. Molloy, et al.. (2024). A new generation of nanobody research tools using improved mass spectrometry-based discovery methods. Journal of Biological Chemistry. 300(9). 107623–107623. 6 indexed citations

Goldberg, Gregory W., Sarah Keegan, Max A. B. Haase, et al.. (2024). Engineered transcription-associated Cas9 targeting in eukaryotic cells. Nature Communications. 15(1). 10287–10287. 1 indexed citations

Liu, Wenke, et al.. (2023). Prediction of Shoulder Dystocia Utilizing Machine Learning. American Journal of Obstetrics and Gynecology. 228(1). S202–S202. 1 indexed citations

McKerrow, Wilson, Nicole Doudican, Shane A. Evans, et al.. (2023). LINE-1 retrotransposon expression in cancerous, epithelial and neuronal cells revealed by 5′ single-cell RNA-Seq. Nucleic Acids Research. 51(5). 2033–2045. 12 indexed citations

Schraink, Tobias, Lili M. Blumenberg, Tania J González-Robles, et al.. (2023). PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data. Molecular & Cellular Proteomics. 22(8). 100596–100596.

Johannet, Paul, Wenke Liu, David Fenyö, et al.. (2022). Baseline Serum Autoantibody Signatures Predict Recurrence and Toxicity in Melanoma Patients Receiving Adjuvant Immune Checkpoint Blockade. Clinical Cancer Research. 28(18). 4121–4130. 30 indexed citations

Hong, Runyu & David Fenyö. (2022). Deep Learning and Its Applications in Computational Pathology. BioMedInformatics. 2(1). 159–168. 10 indexed citations

Hong, Runyu, et al.. (2021). Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Reports Medicine. 2(9). 100400–100400. 80 indexed citations

10.

Hong, Runyu, et al.. (2021). Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning. BioMedInformatics. 2(1). 101–105. 3 indexed citations

11.

Kim, Randie H., Sofia Nomikou, Nicolas Coudray, et al.. (2021). Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas. Journal of Investigative Dermatology. 142(6). 1650–1658.e6. 27 indexed citations

12.

Kalmykova, Svetlana, Hua Jiang, Kelly R. Molloy, et al.. (2020). Affinity proteomic dissection of the human nuclear cap-binding complex interactome. Nucleic Acids Research. 48(18). 10456–10469. 22 indexed citations

13.

Pérez-Hernández, Marta, Alejandra Leo‐Macías, Sarah Keegan, et al.. (2020). Structural and Functional Characterization of a Na _v 1.5-Mitochondrial Couplon. Circulation Research. 128(3). 419–432. 20 indexed citations

14.

Ardeljan, Daniel, Jared P. Steranka, Chunhong Liu, et al.. (2020). Cell fitness screens reveal a conflict between LINE-1 retrotransposition and DNA replication. Nature Structural & Molecular Biology. 27(2). 168–178. 80 indexed citations

15.

McKerrow, Wilson & David Fenyö. (2019). L1EM: a tool for accurate locus specific LINE-1 RNA quantification. Bioinformatics. 36(4). 1167–1173. 29 indexed citations

16.

Adney, Emily M., Srinjoy Sil, David M. Truong, et al.. (2019). Comprehensive Scanning Mutagenesis of Human Retrotransposon LINE-1 Identifies Motifs Essential for Function. Genetics. 213(4). 1401–1414. 18 indexed citations

17.

Agmon, Neta, Zuojian Tang, Tobias Schraink, et al.. (2019). Phylogenetic debugging of a complete human biosynthetic pathway transplanted into yeast. Nucleic Acids Research. 48(1). 486–499. 12 indexed citations

18.

Sun, Xiaoji, Xuya Wang, Zuojian Tang, et al.. (2018). Transcription factor profiling reveals molecular choreography and key regulators of human retrotransposon expression. Proceedings of the National Academy of Sciences. 115(24). E5526–E5535. 65 indexed citations

19.

Reid, Dylan A., Sarah Keegan, Alejandra Leo‐Macías, et al.. (2015). Organization and dynamics of the nonhomologous end-joining machinery during DNA double-strand break repair. Proceedings of the National Academy of Sciences. 112(20). E2575–84. 131 indexed citations

20.

Ueberheide, Beatrix, Borko Amulic, David Fenyö, et al.. (2013). Atypical and classical memory B cells produce Plasmodium falciparum neutralizing antibodies. The Journal of Experimental Medicine. 210(2). 389–399. 156 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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