Emilio Fenoy

406 total citations
8 papers, 254 citations indexed

About

Emilio Fenoy is a scholar working on Molecular Biology, Pharmacology and Computational Theory and Mathematics. According to data from OpenAlex, Emilio Fenoy has authored 8 papers receiving a total of 254 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 1 paper in Pharmacology and 1 paper in Computational Theory and Mathematics. Recurrent topics in Emilio Fenoy's work include RNA and protein synthesis mechanisms (4 papers), Machine Learning in Bioinformatics (3 papers) and Genomics and Phylogenetic Studies (3 papers). Emilio Fenoy is often cited by papers focused on RNA and protein synthesis mechanisms (4 papers), Machine Learning in Bioinformatics (3 papers) and Genomics and Phylogenetic Studies (3 papers). Emilio Fenoy collaborates with scholars based in Argentina, Denmark and Spain. Emilio Fenoy's co-authors include Morten Nielsen, Michael Rasmussen, Anne B. Kristensen, Søren Buus, Mikkel Harndahl, Georgina Stegmayer, Diego H. Milone, Leandro A. Bugnon, Leandro E. Di Persia and M. Gérard and has published in prestigious journals such as Bioinformatics, The Journal of Immunology and Briefings in Bioinformatics.

In The Last Decade

Emilio Fenoy

8 papers receiving 252 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Emilio Fenoy Argentina	5	217	119	55	49	26	8	254
Austin Crinklaw United States	4	212 1.0×	182 1.5×	70 1.3×	55 1.1×	8 0.3×	6	311
Igor Snapkov Norway	10	260 1.2×	160 1.3×	153 2.8×	68 1.4×	25 1.0×	14	405
Mark Yarmarkovich United States	8	178 0.8×	96 0.8×	68 1.2×	43 0.9×	43 1.7×	10	258
Marie Locard‐Paulet France	12	219 1.0×	61 0.5×	14 0.3×	58 1.2×	15 0.6×	29	332
Grant L. J. Keller United States	9	195 0.9×	228 1.9×	98 1.8×	113 2.3×	13 0.5×	11	298
Sneha Rangarajan United States	10	100 0.5×	135 1.1×	96 1.7×	38 0.8×	14 0.5×	10	312
Megan N. Thomas United States	4	97 0.4×	100 0.8×	49 0.9×	68 1.4×	20 0.8×	8	174
Ernest C. So United Kingdom	6	258 1.2×	134 1.1×	8 0.1×	77 1.6×	29 1.1×	7	393
Valérie Calabro United States	8	269 1.2×	75 0.6×	145 2.6×	28 0.6×	9 0.3×	12	353
Marina E. Borisova Germany	8	269 1.2×	44 0.4×	11 0.2×	59 1.2×	44 1.7×	10	339

Countries citing papers authored by Emilio Fenoy

Since Specialization

Citations

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

Fields of papers citing papers by Emilio Fenoy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emilio Fenoy

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

All Works

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8 of 8 papers shown

1.

Bugnon, Leandro A., Leandro E. Di Persia, M. Gérard, et al.. (2024). sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure. Briefings in Bioinformatics. 25(4). 4 indexed citations

2.

Bugnon, Leandro A., et al.. (2024). Evaluating large language models for annotating proteins. Briefings in Bioinformatics. 25(3). 4 indexed citations

3.

Bugnon, Leandro A., et al.. (2023). Transfer learning: The key to functionally annotate the protein universe. Patterns. 4(2). 100691–100691. 4 indexed citations

4.

Bugnon, Leandro A., M. Gérard, Emilio Fenoy, et al.. (2022). Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches. Briefings in Bioinformatics. 23(4). 30 indexed citations

5.

Fenoy, Emilio, et al.. (2022). Transfer learning in proteins: evaluating novel protein learned representations for bioinformatics tasks. Briefings in Bioinformatics. 23(4). 26 indexed citations

6.

Morrison, W. Ivan, Tara A. Sheldrake, Kyle Tretina, et al.. (2021). CD4 T Cell Responses to Theileria parva in Immune Cattle Recognize a Diverse Set of Parasite Antigens Presented on the Surface of Infected Lymphoblasts. The Journal of Immunology. 207(8). 1965–1977. 5 indexed citations

7.

Fenoy, Emilio, José M. G. Izarzugaza, Vanessa Jurtz, Søren Brunak, & Morten Nielsen. (2018). A generic deep convolutional neural network framework for prediction of receptor–ligand interactions—NetPhosPan: application to kinase phosphorylation prediction. Bioinformatics. 35(7). 1098–1107. 17 indexed citations

8.

Rasmussen, Michael, Emilio Fenoy, Mikkel Harndahl, et al.. (2016). Pan-Specific Prediction of Peptide–MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity. The Journal of Immunology. 197(4). 1517–1524. 164 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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