Gabriel del Rio

6.2k total citations · 3 hit papers
45 papers, 3.2k citations indexed

About

Gabriel del Rio is a scholar working on Molecular Biology, Microbiology and Cell Biology. According to data from OpenAlex, Gabriel del Rio has authored 45 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 13 papers in Microbiology and 7 papers in Cell Biology. Recurrent topics in Gabriel del Rio's work include Antimicrobial Peptides and Activities (13 papers), Machine Learning in Bioinformatics (9 papers) and RNA Interference and Gene Delivery (8 papers). Gabriel del Rio is often cited by papers focused on Antimicrobial Peptides and Activities (13 papers), Machine Learning in Bioinformatics (9 papers) and RNA Interference and Gene Delivery (8 papers). Gabriel del Rio collaborates with scholars based in Mexico, United States and Germany. Gabriel del Rio's co-authors include Dale E. Bredesen, Rammohan V. Rao, H. Michael Ellerby, Lisa Ellerby, Susana Castro‐Obregón, Evan Hermel, Wadih Arap, Renata Pasqualini, Renate Kain and Christian R. Lombardo and has published in prestigious journals such as Journal of Biological Chemistry, Nature Medicine and The EMBO Journal.

In The Last Decade

Gabriel del Rio

43 papers receiving 3.1k citations

Hit Papers

Anti-cancer activity of t... 1999 2026 2008 2017 1999 2001 2002 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gabriel del Rio Mexico 19 2.2k 1.0k 593 367 303 45 3.2k
James M. Staddon United Kingdom 27 1.9k 0.9× 540 0.5× 271 0.5× 365 1.0× 278 0.9× 52 3.7k
Seisuke Hattori Japan 35 2.7k 1.2× 724 0.7× 274 0.5× 555 1.5× 310 1.0× 97 3.9k
Kenji Yamamoto Japan 37 1.7k 0.8× 476 0.5× 252 0.4× 526 1.4× 233 0.8× 155 3.9k
Elena Miranda Italy 29 2.1k 0.9× 1.0k 1.0× 324 0.5× 253 0.7× 345 1.1× 84 3.8k
Koichi Honke Japan 42 3.7k 1.7× 925 0.9× 227 0.4× 1.2k 3.4× 385 1.3× 134 5.2k
Michael Rehman Italy 14 3.4k 1.6× 384 0.4× 380 0.6× 229 0.6× 201 0.7× 16 4.1k
Albert Pol Spain 36 2.8k 1.3× 1.6k 1.5× 479 0.8× 304 0.8× 147 0.5× 76 4.3k
Lone Bastholm Denmark 19 2.0k 0.9× 712 0.7× 1.2k 2.0× 638 1.7× 114 0.4× 31 3.6k
Benjamin L. Parker Australia 37 3.4k 1.6× 611 0.6× 372 0.6× 323 0.9× 153 0.5× 99 4.6k
John H. Walker United Kingdom 40 2.9k 1.3× 730 0.7× 195 0.3× 725 2.0× 567 1.9× 149 4.6k

Countries citing papers authored by Gabriel del Rio

Since Specialization
Citations

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

Fields of papers citing papers by Gabriel del Rio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gabriel del Rio. 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 Gabriel del Rio. The network helps show where Gabriel del Rio may publish in the future.

Co-authorship network of co-authors of Gabriel del Rio

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriel del Rio. A scholar is included among the top collaborators of Gabriel del Rio 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 Gabriel del Rio. Gabriel del Rio 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
2.
Rio, Gabriel del, et al.. (2020). An automatic representation of peptides for effective antimicrobial activity classification. Computational and Structural Biotechnology Journal. 18. 455–463. 12 indexed citations
3.
Moreno-Blas, Daniel, Miguel Costas, Christian Diener, et al.. (2020). Antimicrobial Peptide against Mycobacterium Tuberculosis That Activates Autophagy Is an Effective Treatment for Tuberculosis. Pharmaceutics. 12(11). 1071–1071. 20 indexed citations
4.
Brizuela, Carlos A., et al.. (2019). Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs. Molecules. 24(7). 1258–1258. 14 indexed citations
5.
Rio, Gabriel del, Edda Klipp, & Andreas Herrmann. (2016). Using Confocal Microscopy and Computational Modeling to Investigate the Cell-Penetrating Properties of Antimicrobial Peptides. Methods in molecular biology. 1548. 191–199. 4 indexed citations
6.
Campo, Ana Martínez-del, et al.. (2015). Quality Control Test for Sequence-Phenotype Assignments. PLoS ONE. 10(2). e0118288–e0118288. 1 indexed citations
7.
Diener, Christian, Omar Pantoja, Rudolf Volkmer, et al.. (2014). Cell Penetrating Peptides and Cationic Antibacterial Peptides. Journal of Biological Chemistry. 289(21). 14448–14457. 40 indexed citations
8.
Diener, Christian, et al.. (2014). Yeast Mating and Image-Based Quantification of Spatial Pattern Formation. PLoS Computational Biology. 10(6). e1003690–e1003690. 27 indexed citations
9.
Paredes, Sur Herrera, Georgina Garza‐Ramos, Alfredo Torres‐Larios, et al.. (2012). Moonlighting Peptides with Emerging Function. PLoS ONE. 7(7). e40125–e40125. 16 indexed citations
10.
Rio, Gabriel del, et al.. (2009). How to identify essential genes from molecular networks?. BMC Systems Biology. 3(1). 102–102. 69 indexed citations
11.
Cusack, Michael P., et al.. (2007). Efficient Identification of Critical Residues Based Only on Protein Structure by Network Analysis. PLoS ONE. 2(5). e421–e421. 25 indexed citations
12.
Llambi, Fabien, Filipe C. Lourenço, Devrim Gözüaçık, et al.. (2005). The dependence receptor UNC5H2 mediates apoptosis through DAP‐kinase. The EMBO Journal. 24(6). 1192–1201. 136 indexed citations
13.
Castro‐Obregón, Susana, Rammohan V. Rao, Gabriel del Rio, et al.. (2004). Alternative, Nonapoptotic Programmed Cell Death. Journal of Biological Chemistry. 279(17). 17543–17553. 77 indexed citations
14.
Jin, Kunlin, Xiao Mao, Mark W. Eshoo, et al.. (2002). cDNA Microarray Analysis of Changes in Gene Expression Induced by Neuronal Hypoxia in Vitro. Neurochemical Research. 27(10). 1105–1112. 63 indexed citations
15.
Bredesen, Dale E., Harald Frankowski, Susana Castro‐Obregón, Gabriel del Rio, & Rammohan V. Rao. (2002). PLAIDD,a Type II Death Domain Protein that Interacts with p75 Neurotrophin Receptor. NeuroMolecular Medicine. 1(3). 153–170. 27 indexed citations
16.
Castro‐Obregón, Susana, Gabriel del Rio, Raymond A. Swanson, et al.. (2002). A ligand-receptor pair that triggers a non-apoptotic form of programmed cell death. Cell Death and Differentiation. 9(8). 807–817. 59 indexed citations
17.
Rao, Rammohan V., Susana Castro‐Obregón, Harald Frankowski, et al.. (2002). Coupling Endoplasmic Reticulum Stress to the Cell Death Program. Journal of Biological Chemistry. 277(24). 21836–21842. 409 indexed citations
18.
Rao, Rammohan V., Evan Hermel, Susana Castro‐Obregón, et al.. (2001). Coupling Endoplasmic Reticulum Stress to the Cell Death Program. Journal of Biological Chemistry. 276(36). 33869–33874. 509 indexed citations breakdown →
19.
Rio, Gabriel del, et al.. (1991). UBV photometry of the open cluster in the Cassiopeia region. I. Photoelectric observations of NGC 436 and 637.. Astronomy & Astrophysics Supplement Series. 87(1). 153–158. 1 indexed citations
20.
Rio, Gabriel del, et al.. (1988). Photoelectric UBV and photographic RGU photometry of the open clusters NGC 1496 and NGC 1513.. Astronomy & Astrophysics Supplement Series. 73(3). 425–435.

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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