Ottavia Spiga

4.3k total citations
119 papers, 2.2k citations indexed

About

Ottavia Spiga is a scholar working on Molecular Biology, Clinical Biochemistry and Genetics. According to data from OpenAlex, Ottavia Spiga has authored 119 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Molecular Biology, 19 papers in Clinical Biochemistry and 15 papers in Genetics. Recurrent topics in Ottavia Spiga's work include Protein Structure and Dynamics (21 papers), Metabolism and Genetic Disorders (19 papers) and Enzyme Structure and Function (15 papers). Ottavia Spiga is often cited by papers focused on Protein Structure and Dynamics (21 papers), Metabolism and Genetic Disorders (19 papers) and Enzyme Structure and Function (15 papers). Ottavia Spiga collaborates with scholars based in Italy, United Kingdom and Vietnam. Ottavia Spiga's co-authors include Andrea Bernini, Neri Niccolai, Alfonso Trezza, Annalisa Santucci, Fabio Fusi, Arianna Ciutti, Simona Saponara, Filippo Prischi, Vittoria Cicaloni and Vincenzo Venditti and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Biological Chemistry and SHILAP Revista de lepidopterología.

In The Last Decade

Ottavia Spiga

114 papers receiving 2.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ottavia Spiga Italy 25 1.2k 437 244 207 200 119 2.2k
Stewart L. Fisher United States 29 1.8k 1.5× 325 0.7× 349 1.4× 43 0.2× 213 1.1× 53 3.7k
Anna Marabotti Italy 26 1.0k 0.9× 120 0.3× 109 0.4× 194 0.9× 358 1.8× 100 1.9k
Carlos Červeñanský Uruguay 25 1.4k 1.2× 343 0.8× 395 1.6× 34 0.2× 129 0.6× 46 2.3k
Lucy R. Forrest United States 41 3.8k 3.2× 463 1.1× 147 0.6× 113 0.5× 412 2.1× 95 5.0k
Eric B. Fauman United States 25 2.5k 2.1× 382 0.9× 137 0.6× 29 0.1× 467 2.3× 41 3.4k
Aditi Das United States 35 2.2k 1.8× 179 0.4× 65 0.3× 52 0.3× 195 1.0× 108 3.8k
Brian W. Metcalf United States 34 2.5k 2.1× 355 0.8× 881 3.6× 217 1.0× 125 0.6× 81 4.9k
Walter H. Moos United States 30 2.7k 2.2× 124 0.3× 223 0.9× 56 0.3× 120 0.6× 80 3.9k
David L. Selwood United Kingdom 32 1.9k 1.6× 136 0.3× 293 1.2× 50 0.2× 51 0.3× 100 3.7k
Knut Teigen Norway 21 1.6k 1.3× 112 0.3× 49 0.2× 213 1.0× 228 1.1× 49 2.3k

Countries citing papers authored by Ottavia Spiga

Since Specialization
Citations

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

Fields of papers citing papers by Ottavia Spiga

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ottavia Spiga

This figure shows the co-authorship network connecting the top 25 collaborators of Ottavia Spiga. A scholar is included among the top collaborators of Ottavia Spiga 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 Ottavia Spiga. Ottavia Spiga 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.
Fusar‐Poli, Paolo, et al.. (2025). Predicting therapy dropout in chronic pain management: a machine learning approach to cannabis treatment. Frontiers in Artificial Intelligence. 8. 1557894–1557894. 1 indexed citations
2.
Trezza, Alfonso, et al.. (2025). Unveiling Dynamic Hotspots in Protein–Ligand Binding: Accelerating Target and Drug Discovery Approaches. International Journal of Molecular Sciences. 26(9). 3971–3971. 2 indexed citations
3.
Spiga, Ottavia, et al.. (2025). Profiling of Protein-Coding Missense Mutations in Mendelian Rare Diseases: Clues from Structural Bioinformatics. International Journal of Molecular Sciences. 26(9). 4072–4072.
4.
Parisi, Maria Laura, et al.. (2025). A combined ML and DFT strategy for the prediction of dye candidates for indoor DSSCs. npj Computational Materials. 11(1). 3 indexed citations
5.
Trezza, Alfonso, et al.. (2024). Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI. Applied Sciences. 14(20). 9305–9305. 15 indexed citations
6.
Trezza, Alfonso, Michela Geminiani, Elena Dreassi, et al.. (2024). A Drug Discovery Approach to a Reveal Novel Antioxidant Natural Source: The Case of Chestnut Burr Biomass. International Journal of Molecular Sciences. 25(5). 2517–2517. 9 indexed citations
7.
Son, Ninh The, et al.. (2024). 3,3′-O-dimethylquercetin: A bi-functional vasodilator isolated from green propolis of the Caatinga Mimosa tenuiflora. European Journal of Pharmacology. 967. 176400–176400. 4 indexed citations
8.
Geminiani, Michela, Alfonso Trezza, Laura Salvini, et al.. (2024). Phytochemical Composition, Anti-Inflammatory Property, and Anti-Atopic Effect of Chaetomorpha linum Extract. Marine Drugs. 22(5). 226–226. 11 indexed citations
9.
10.
Carullo, Gabriele, Ottavia Spiga, Amer Ahmed, et al.. (2024). Exploring the chemical space around chrysin to develop novel vascular CaV1.2 channel blockers, promising vasorelaxant agents. Archiv der Pharmazie. 357(11). e2400536–e2400536. 1 indexed citations
11.
Spiga, Ottavia, et al.. (2023). The Impact of Artificial Intelligence in the Odyssey of Rare Diseases. Biomedicines. 11(3). 887–887. 49 indexed citations
12.
Fusar‐Poli, Paolo, et al.. (2023). Supporting Machine Learning Model in the Treatment of Chronic Pain. Biomedicines. 11(7). 1776–1776. 6 indexed citations
13.
Cường, Nguyễn Mạnh, Ninh The Son, Yoshiyasu Fukuyama, et al.. (2022). Vietnamese Dalbergia tonkinensis: A Promising Source of Mono- and Bifunctional Vasodilators. Molecules. 27(14). 4505–4505. 5 indexed citations
14.
Rossi, Martina, Vittoria Cicaloni, Ranieri Rossi, et al.. (2022). Homogentisic acid induces autophagy alterations leading to chondroptosis in human chondrocytes: Implications in Alkaptonuria. Archives of Biochemistry and Biophysics. 717. 109137–109137. 9 indexed citations
15.
Geminiani, Michela, et al.. (2022). Artificial Intelligence Approaches in Drug Discovery: Towards the Laboratoryof the Future. Current Topics in Medicinal Chemistry. 22(26). 2176–2189. 8 indexed citations
16.
Trabalzini, Lorenza, et al.. (2022). Pharmacological and In Silico Analysis of Oat Avenanthramides as EGFR Inhibitors: Effects on EGF-Induced Lung Cancer Cell Growth and Migration. International Journal of Molecular Sciences. 23(15). 8534–8534. 12 indexed citations
17.
Bisi, Alessandra, Alessandra Feoli, Alfonso Trezza, et al.. (2022). Targeting neuronal calcium channels and GSK3β for Alzheimer’s disease with naturally-inspired Diels-Alder adducts. Bioorganic Chemistry. 129. 106152–106152. 7 indexed citations
18.
Cicaloni, Vittoria, et al.. (2021). Multi-Omics Model Applied to Cancer Genetics. International Journal of Molecular Sciences. 22(11). 5751–5751. 30 indexed citations
19.
Rossi, Alberto, Vittoria Cicaloni, Andrea Bernini, et al.. (2020). AKUImg: A database of cartilage images of Alkaptonuria patients. Computers in Biology and Medicine. 122. 103863–103863. 10 indexed citations
20.
Venditti, Vincenzo, Andrea Bernini, Alfonso De Simone, et al.. (2007). MD and NMR studies of α-bungarotoxin surface accessibility. Biochemical and Biophysical Research Communications. 356(1). 114–117. 14 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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