Alfonso Urso

1.3k total citations
54 papers, 564 citations indexed

About

Alfonso Urso is a scholar working on Molecular Biology, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Alfonso Urso has authored 54 papers receiving a total of 564 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 14 papers in Computer Networks and Communications and 10 papers in Artificial Intelligence. Recurrent topics in Alfonso Urso's work include Genomics and Phylogenetic Studies (9 papers), MicroRNA in disease regulation (7 papers) and Scientific Computing and Data Management (6 papers). Alfonso Urso is often cited by papers focused on Genomics and Phylogenetic Studies (9 papers), MicroRNA in disease regulation (7 papers) and Scientific Computing and Data Management (6 papers). Alfonso Urso collaborates with scholars based in Italy, India and United Kingdom. Alfonso Urso's co-authors include Antonino Fiannaca, Massimo La Rosa, Riccardo Rizzo, Laura La Paglia, Salvatore Gaglio, Giuseppe Lo Re, Giuseppe Di Fatta, Giosuè Lo Bosco, Frank Hoffmann and Antonio Messina and has published in prestigious journals such as Scientific Reports, International Journal of Molecular Sciences and Expert Systems with Applications.

In The Last Decade

Alfonso Urso

48 papers receiving 546 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alfonso Urso Italy 14 346 109 67 63 31 54 564
Kenji Satou Japan 18 659 1.9× 199 1.8× 135 2.0× 26 0.4× 48 1.5× 95 1.1k
Brandon Malone United States 13 291 0.8× 55 0.5× 168 2.5× 32 0.5× 22 0.7× 29 605
Sean A. Irvine United Kingdom 7 268 0.8× 60 0.6× 105 1.6× 59 0.9× 7 0.2× 13 539
Yuhan Cai China 11 185 0.5× 34 0.3× 108 1.6× 75 1.2× 25 0.8× 34 601
Antonino Fiannaca Italy 14 338 1.0× 106 1.0× 54 0.8× 13 0.2× 21 0.7× 37 471
Ralf Hofestädt Germany 14 536 1.5× 48 0.4× 82 1.2× 38 0.6× 16 0.5× 98 799
Aleksi Kallio Finland 12 254 0.7× 65 0.6× 64 1.0× 59 0.9× 8 0.3× 25 416
Len Trigg New Zealand 5 735 2.1× 178 1.6× 105 1.6× 15 0.2× 24 0.8× 8 1.1k
Yizhou Li China 15 562 1.6× 63 0.6× 62 0.9× 18 0.3× 14 0.5× 62 746

Countries citing papers authored by Alfonso Urso

Since Specialization
Citations

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

Fields of papers citing papers by Alfonso Urso

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alfonso Urso

This figure shows the co-authorship network connecting the top 25 collaborators of Alfonso Urso. A scholar is included among the top collaborators of Alfonso Urso 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 Alfonso Urso. Alfonso Urso 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.
Fiannaca, Antonino, et al.. (2025). GL4SDA: Predicting snoRNA-disease associations using GNNs and LLM embeddings. Computational and Structural Biotechnology Journal. 27. 1023–1033.
2.
Paglia, Laura La, Manuela Mauro, Vincenzo Arizza, et al.. (2025). Bioinformatics analyses of the proteome of Holothuria tubulosa coelomic fluid and the first evidence of primary cilium in coelomocyte cells. Frontiers in Immunology. 16. 1539751–1539751. 1 indexed citations
3.
4.
Fiannaca, Antonino, et al.. (2024). Explainable artificial intelligence models for key-metabolites identification in overweight subjects. Procedia Computer Science. 246. 1963–1972.
5.
Fiannaca, Antonino, Massimo La Rosa, Laura La Paglia, Salvatore Gaglio, & Alfonso Urso. (2023). GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data. Briefings in Bioinformatics. 24(6). 2 indexed citations
6.
Longo, Valeria, Elena Lo Presti, Antonino Fiannaca, et al.. (2023). Impact of the flame retardant 2,2’4,4’-tetrabromodiphenyl ether (PBDE-47) in THP-1 macrophage-like cell function via small extracellular vesicles. Frontiers in Immunology. 13. 1069207–1069207. 13 indexed citations
7.
Paglia, Laura La, Mirella Vazzana, Manuela Mauro, et al.. (2023). Transcriptomic and Bioinformatic Analyses Identifying a Central Mif-Cop9-Nf-kB Signaling Network in Innate Immunity Response of Ciona robusta. International Journal of Molecular Sciences. 24(4). 4112–4112. 3 indexed citations
8.
Paglia, Laura La, Mirella Vazzana, Manuela Mauro, et al.. (2023). Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence. Marine Drugs. 22(1). 6–6. 2 indexed citations
9.
10.
Fiannaca, Antonino, Laura La Paglia, Massimo La Rosa, Riccardo Rizzo, & Alfonso Urso. (2020). miRTissue ce: extending miRTissue web service with the analysis of ceRNA-ceRNA interactions. BMC Bioinformatics. 21(S8). 199–199. 13 indexed citations
11.
Arizza, Vincenzo, Angela Bonura, Laura La Paglia, et al.. (2020). Transcriptional and in silico analyses of MIF cytokine and TLR signalling interplay in the LPS inflammatory response of Ciona robusta. Scientific Reports. 10(1). 11339–11339. 13 indexed citations
12.
Boscaino, V., Antonino Fiannaca, Laura La Paglia, et al.. (2019). MiRNA therapeutics based on logic circuits of biological pathways. BMC Bioinformatics. 20(S9). 344–344. 14 indexed citations
13.
Fiannaca, Antonino, Massimo La Rosa, Laura La Paglia, & Alfonso Urso. (2018). miRTissue: a web application for the analysis of miRNA-target interactions in human tissues. BMC Bioinformatics. 19(S15). 434–434. 6 indexed citations
14.
Fiannaca, Antonino, Laura La Paglia, Massimo La Rosa, et al.. (2018). Deep learning models for bacteria taxonomic classification of metagenomic data. BMC Bioinformatics. 19(S7). 198–198. 81 indexed citations
15.
Fiannaca, Antonino, Massimo La Rosa, Laura La Paglia, Riccardo Rizzo, & Alfonso Urso. (2017). nRC: non-coding RNA Classifier based on structural features. BioData Mining. 10(1). 27–27. 52 indexed citations
16.
Fiannaca, Antonino, Massimo La Rosa, Laura La Paglia, Riccardo Rizzo, & Alfonso Urso. (2016). MiRNATIP: a SOM-based miRNA-target interactions predictor. BMC Bioinformatics. 17(S11). 321–321. 10 indexed citations
17.
Fiannaca, Antonino, Massimo La Rosa, Alfonso Urso, Riccardo Rizzo, & Salvatore Gaglio. (2013). A knowledge-based decision support system in bioinformatics: an application to protein complex extraction. BMC Bioinformatics. 14(S1). S5–S5. 14 indexed citations
18.
Rosa, Massimo La, Antonino Fiannaca, Riccardo Rizzo, & Alfonso Urso. (2013). Alignment-free analysis of barcode sequences by means of compression-based methods. BMC Bioinformatics. 14(S7). S4–S4. 16 indexed citations
19.
Rosa, Massimo La, Antonino Fiannaca, Riccardo Rizzo, & Alfonso Urso. (2012). Analysis of barcode sequences by means of compression-based methods. EMBnet journal. 18(A). 92–92. 1 indexed citations
20.
Cossentino, Massimo, et al.. (2007). DNK-WSD: A Distributed Approach for Knowledge Discovery in Peer to Peer Networks. 325–332. 1 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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