Fernando Ramón

486 total citations
18 papers, 363 citations indexed

About

Fernando Ramón is a scholar working on Molecular Biology, Biochemistry and Immunology. According to data from OpenAlex, Fernando Ramón has authored 18 papers receiving a total of 363 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 4 papers in Biochemistry and 4 papers in Immunology. Recurrent topics in Fernando Ramón's work include Amino Acid Enzymes and Metabolism (4 papers), Polyamine Metabolism and Applications (4 papers) and Tuberculosis Research and Epidemiology (3 papers). Fernando Ramón is often cited by papers focused on Amino Acid Enzymes and Metabolism (4 papers), Polyamine Metabolism and Applications (4 papers) and Tuberculosis Research and Epidemiology (3 papers). Fernando Ramón collaborates with scholars based in Spain, United Kingdom and France. Fernando Ramón's co-authors include Isabel de la Mata, Carmen Acebal, M.P. Castillón, Gonzalo Colmenarejo, Esther Pérez‐Herrán, Joel S. Bader, Petros C. Karakousis, Harvey Rubin, José M. Mancheño and Jae Jin Lee and has published in prestigious journals such as Scientific Reports, Biochemical Journal and Biophysical Journal.

In The Last Decade

Fernando Ramón

18 papers receiving 355 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fernando Ramón Spain 12 211 83 69 57 42 18 363
Jeff D. Moore United States 9 342 1.6× 78 0.9× 51 0.7× 89 1.6× 19 0.5× 10 552
Tao Cui United States 11 424 2.0× 35 0.4× 40 0.6× 26 0.5× 71 1.7× 19 639
Ana Carolina Ramos Guimarães Brazil 12 249 1.2× 65 0.8× 124 1.8× 23 0.4× 20 0.5× 39 471
Rachel E. Rigsby United States 5 226 1.1× 34 0.4× 65 0.9× 33 0.6× 77 1.8× 5 419
Matteo Santucci Italy 14 241 1.1× 65 0.8× 98 1.4× 23 0.4× 78 1.9× 26 540
Sandra Lightle United States 10 279 1.3× 43 0.5× 27 0.4× 45 0.8× 40 1.0× 10 469
Brent A. Biddy United States 5 322 1.5× 43 0.5× 25 0.4× 26 0.5× 74 1.8× 5 503
Maryelle Tropis France 11 239 1.1× 120 1.4× 126 1.8× 16 0.3× 43 1.0× 16 384
Abhijit Roychowdhury India 15 219 1.0× 33 0.4× 51 0.7× 234 4.1× 26 0.6× 27 688
Rahul Modak India 12 223 1.1× 42 0.5× 31 0.4× 29 0.5× 19 0.5× 29 466

Countries citing papers authored by Fernando Ramón

Since Specialization
Citations

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

Fields of papers citing papers by Fernando Ramón

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fernando Ramón

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

All Works

18 of 18 papers shown
1.
Li, Shamin, Chahrazade Kantari‐Mimoun, Samantha Knockaert, et al.. (2024). In silico and pharmacological evaluation of GPR65 as a cancer immunotherapy target regulating T-cell functions. Frontiers in Immunology. 15. 1483258–1483258. 2 indexed citations
2.
Hormigo, Daniel, Jesús Fernández‐Lucas, Pedro Torres‐Ayuso, et al.. (2020). Penicillin Acylase from Streptomyces lavendulae and Aculeacin A Acylase from Actinoplanes utahensis: Two Versatile Enzymes as Useful Tools for Quorum Quenching Processes. Catalysts. 10(7). 730–730. 14 indexed citations
3.
Dutta, Noton K., Lee G. Klinkenberg, Gonzalo Colmenarejo, et al.. (2019). Inhibiting the stringent response blocks Mycobacterium tuberculosis entry into quiescence and reduces persistence. Science Advances. 5(3). eaav2104–eaav2104. 97 indexed citations
4.
Trofimov, Valentin, Sébastien Kicka, Nabil Hanna, et al.. (2018). Antimycobacterial drug discovery using Mycobacteria-infected amoebae identifies anti-infectives and new molecular targets. Scientific Reports. 8(1). 3939–3939. 24 indexed citations
5.
Fernández‐Lucas, Jesús, Carmen Acebal, Miguel Arroyo, et al.. (2018). 2′-Deoxyribosyltransferase from Bacillus psychrosaccharolyticus: A Mesophilic-Like Biocatalyst for the Synthesis of Modified Nucleosides from a Psychrotolerant Bacterium. Catalysts. 8(1). 8–8. 19 indexed citations
6.
Vela, Laura, et al.. (2016). Discovery of Enhancers of the Secretion of Leukemia Inhibitory Factor for the Treatment of Multiple Sclerosis. SLAS DISCOVERY. 21(5). 437–445. 8 indexed citations
7.
Cox, Jonathan A. G., Grace Mugumbate, Monika Jankute, et al.. (2016). Novel inhibitors of Mycobacterium tuberculosis GuaB2 identified by a target based high-throughput phenotypic screen. Scientific Reports. 6(1). 38986–38986. 22 indexed citations
8.
Pérez, Paloma, et al.. (2016). Discovery of Novel Inhibitors of the Tautomerase Activity of Macrophage Migration Inhibitory Factor (MIF). SLAS DISCOVERY. 21(5). 446–458. 14 indexed citations
9.
Gatto, Gregory J., Fernando Ramón, Thomas D. Sweitzer, et al.. (2010). Development of a High-Throughput Cell-Based Assay for Superoxide Production in HL-60 Cells. SLAS DISCOVERY. 15(4). 388–397. 11 indexed citations
10.
Vázquez, María J., Stephen Ashman, Fernando Ramón, et al.. (2006). Utilization of Substrate-Induced Quenching for Screening Targets Promoting NADH and NADPH Consumption. SLAS DISCOVERY. 11(1). 75–81. 6 indexed citations
11.
Jäger, Stefan, Achim K. Kirsch, Kurt Herrenknecht, et al.. (2003). A Modular, Fully Integrated Ultra-High-Throughput Screening System Based on Confocal Fluorescence Analysis Techniques. SLAS DISCOVERY. 8(6). 648–659. 17 indexed citations
12.
Ashman, Stephen, Brian Bond, Fernando Ramón, et al.. (2002). A Standard Operating Procedure for Assessing Liquid Handler Performance in High-Throughput Screening. SLAS DISCOVERY. 7(6). 554–569. 41 indexed citations
13.
Mata, Isabel de la, et al.. (2000). Effect of hydrogen peroxide on d-amino acid oxidase from Rhodotorula gracilis. Enzyme and Microbial Technology. 27(3-5). 234–239. 28 indexed citations
14.
Mata, Isabel de la, et al.. (2000). Chemical modification of tryptophan residues of d-amino acid oxidase from Rhodotorula gracilis. Journal of Molecular Catalysis B Enzymatic. 9(1-3). 65–73. 2 indexed citations
15.
Ramón, Fernando, et al.. (1999). Enhanced production of penicillin V acylase from Streptomyces lavendulae. Applied Microbiology and Biotechnology. 53(1). 81–84. 21 indexed citations
16.
Ramón, Fernando, M.P. Castillón, Isabel de la Mata, & Carmen Acebal. (1998). Chemical mechanism of D-amino acid oxidase from Rhodotorula gracilis: pH dependence of kinetic parameters. Biochemical Journal. 330(1). 311–314. 6 indexed citations
17.
Ramón, Fernando, et al.. (1995). Chemical Modification of Histidyl Residues in D-Amino Acid Oxidase from Rhodotorula gracilis. The Journal of Biochemistry. 118(5). 911–916. 7 indexed citations
18.

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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