Leandro A. Bugnon

401 total citations
22 papers, 251 citations indexed

About

Leandro A. Bugnon is a scholar working on Molecular Biology, Cancer Research and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Leandro A. Bugnon has authored 22 papers receiving a total of 251 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 9 papers in Cancer Research and 3 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Leandro A. Bugnon's work include MicroRNA in disease regulation (7 papers), Cancer-related molecular mechanisms research (7 papers) and Machine Learning in Bioinformatics (5 papers). Leandro A. Bugnon is often cited by papers focused on MicroRNA in disease regulation (7 papers), Cancer-related molecular mechanisms research (7 papers) and Machine Learning in Bioinformatics (5 papers). Leandro A. Bugnon collaborates with scholars based in Argentina, United States and United Kingdom. Leandro A. Bugnon's co-authors include Diego H. Milone, Georgina Stegmayer, Leandro E. Di Persia, M. Gérard, Federico Ariel, Emilio Fenoy, Gabriela Merino, Laura Kamenetzky, Rafael A. Calvo and Milton Pividori and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Leandro A. Bugnon

22 papers receiving 250 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Leandro A. Bugnon Argentina 11 151 88 34 28 17 22 251
Neelima Arora India 10 133 0.9× 26 0.3× 30 0.9× 21 0.8× 25 1.5× 31 283
Ulykbek Kairov Kazakhstan 9 144 1.0× 19 0.2× 40 1.2× 21 0.8× 18 1.1× 47 284
Yishu Li China 12 106 0.7× 77 0.9× 30 0.9× 35 1.3× 4 0.2× 36 319
Guanglin Niu China 12 268 1.8× 223 2.5× 12 0.4× 41 1.5× 14 0.8× 19 400
Shibin Qiu United States 5 200 1.3× 43 0.5× 8 0.2× 55 2.0× 31 1.8× 8 339
Nicolae Sapoval United States 6 167 1.1× 14 0.2× 76 2.2× 27 1.0× 17 1.0× 12 266
Ankita Singh India 10 250 1.7× 37 0.4× 14 0.4× 9 0.3× 27 1.6× 23 338
Fethullah Karabiber Türkiye 9 315 2.1× 36 0.4× 20 0.6× 23 0.8× 84 4.9× 20 457
Fariza Tahi France 13 391 2.6× 134 1.5× 26 0.8× 11 0.4× 77 4.5× 35 502
Roy Ronen United States 12 304 2.0× 118 1.3× 16 0.5× 13 0.5× 64 3.8× 16 485

Countries citing papers authored by Leandro A. Bugnon

Since Specialization
Citations

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

Fields of papers citing papers by Leandro A. Bugnon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leandro A. Bugnon. 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 Leandro A. Bugnon. The network helps show where Leandro A. Bugnon may publish in the future.

Co-authorship network of co-authors of Leandro A. Bugnon

This figure shows the co-authorship network connecting the top 25 collaborators of Leandro A. Bugnon. A scholar is included among the top collaborators of Leandro A. Bugnon 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 Leandro A. Bugnon. Leandro A. Bugnon 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
1.
Bugnon, Leandro A., et al.. (2025). Comprehensive benchmarking of large language models for RNA secondary structure prediction. Briefings in Bioinformatics. 26(2). 6 indexed citations
2.
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
3.
Bugnon, Leandro A., et al.. (2024). Evaluating large language models for annotating proteins. Briefings in Bioinformatics. 25(3). 4 indexed citations
4.
Bollazzi, Martín, et al.. (2023). AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior. SHILAP Revista de lepidopterología. 5. 100252–100252. 1 indexed citations
5.
Bugnon, Leandro A., et al.. (2023). Transfer learning: The key to functionally annotate the protein universe. Patterns. 4(2). 100691–100691. 4 indexed citations
6.
7.
Bugnon, Leandro A., et al.. (2022). AntVideoRecord: Autonomous system to capture the locomotor activity of leafcutter ants. HardwareX. 11. e00270–e00270. 2 indexed citations
8.
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
9.
Bugnon, Leandro A., et al.. (2021). Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19. SHILAP Revista de lepidopterología. 6. 100150–100150. 13 indexed citations
10.
Bugnon, Leandro A., et al.. (2021). miRe2e: a full end-to-end deep model based on transformers for prediction of pre-miRNAs. Bioinformatics. 38(5). 1191–1197. 20 indexed citations
11.
Bugnon, Leandro A., et al.. (2021). High precision in microRNA prediction: A novel genome-wide approach with convolutional deep residual networks. Computers in Biology and Medicine. 134. 104448–104448. 14 indexed citations
12.
Merino, Gabriela, Leandro A. Bugnon, Laura Kamenetzky, et al.. (2020). Novel SARS-CoV-2 encoded small RNAs in the passage to humans. Bioinformatics. 36(24). 5571–5581. 20 indexed citations
13.
Chelotti, José O., Leandro A. Bugnon, Hugo Leonardo Rufiner, et al.. (2020). Audio recordings dataset of grazing jaw movements in dairy cattle. SHILAP Revista de lepidopterología. 30. 105623–105623. 11 indexed citations
14.
Bugnon, Leandro A., M. Gérard, Gabriela Merino, et al.. (2020). DL4papers: a deep learning approach for the automatic interpretation of scientific articles. Bioinformatics. 36(11). 3499–3506. 4 indexed citations
15.
Bugnon, Leandro A., et al.. (2020). Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning. Briefings in Bioinformatics. 22(3). 12 indexed citations
16.
Bugnon, Leandro A., et al.. (2019). Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics. IEEE Transactions on Neural Networks and Learning Systems. 31(8). 2857–2867. 35 indexed citations
17.
Bugnon, Leandro A., et al.. (2019). Genome-wide hairpins datasets of animals and plants for novel miRNA prediction. SHILAP Revista de lepidopterología. 25. 104209–104209. 5 indexed citations
18.
Macchiaroli, Natalia, Marcela Cucher, Laura Kamenetzky, et al.. (2019). Identification and expression profiling of microRNAs in Hymenolepis. International Journal for Parasitology. 49(3-4). 211–223. 15 indexed citations
19.
Stegmayer, Georgina, Leandro E. Di Persia, M. Gérard, et al.. (2018). Predicting novel microRNA: a comprehensive comparison of machine learning approaches. Briefings in Bioinformatics. 20(5). 1607–1620. 31 indexed citations
20.
Bugnon, Leandro A., Rafael A. Calvo, & Diego H. Milone. (2017). Dimensional Affect Recognition from HRV: An Approach Based on Supervised SOM and ELM. IEEE Transactions on Affective Computing. 11(1). 32–44. 11 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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