Giorgio Valentini

6.1k total citations
127 papers, 2.6k citations indexed

About

Giorgio Valentini is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Giorgio Valentini has authored 127 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Molecular Biology, 32 papers in Artificial Intelligence and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Giorgio Valentini's work include Bioinformatics and Genomic Networks (47 papers), Gene expression and cancer classification (45 papers) and Machine Learning in Bioinformatics (24 papers). Giorgio Valentini is often cited by papers focused on Bioinformatics and Genomic Networks (47 papers), Gene expression and cancer classification (45 papers) and Machine Learning in Bioinformatics (24 papers). Giorgio Valentini collaborates with scholars based in Italy, United States and Germany. Giorgio Valentini's co-authors include Matteo Ré, Thomas G. Dietterich, Alberto Bertoni, Claudia Antonetti, Domenico Licursi, Peter N. Robinson, Francesco Masulli, Sara Fulignati, Nicolò Cesa‐Bianchi and Oleg Okun and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and SHILAP Revista de lepidopterología.

In The Last Decade

Giorgio Valentini

120 papers receiving 2.5k citations

Peers

Giorgio Valentini

CVPR

GENET

Yutong Lu China

Guozheng Li China

Yijia Zhang China

Xiaoyue Wang China

Yu Chen China

Shihua Zhang China

Mengdi Wang China

Yiming Yang China

Elena Marchiori Netherlands

Yutong Lu China

Giorgio Valentini

972 ×0.9

470 ×0.6

339 ×1.0

CVPR

227 ×0.7

54 ×0.2

GENET

Citations per year, relative to Giorgio Valentini Giorgio Valentini (= 1×) peers Yutong Lu

Countries citing papers authored by Giorgio Valentini

Since Specialization

Citations

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

Fields of papers citing papers by Giorgio Valentini

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Giorgio Valentini

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

All Works

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20 of 20 papers shown

Malchiodi, Dario, et al.. (2025). Fine-tuning of conditional Transformers improves in silico enzyme prediction and generation. Computational and Structural Biotechnology Journal. 27. 1318–1334.

Gliozzo, Jessica, Mauricio Soto, Arturo Bonometti, et al.. (2025). miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources. Bioinformatics. 41(4).

Soto, Mauricio, Carlos Cano, Justin Reese, et al.. (2025). Biasing second-order random walk sampling for heterogeneous graph embedding ^*. 1–8. 1 indexed citations

Valentini, Giorgio, et al.. (2025). Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection. 55–60. 1 indexed citations

Soto, Mauricio, Matteo Zignani, Jessica Gliozzo, et al.. (2024). RNA knowledge-graph analysis through homogeneous embedding methods. Bioinformatics Advances. 5(1). vbaf109–vbaf109.

Gliozzo, Jessica, et al.. (2024). RNA Knowledge Graph Analysis via Embedding Methods. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 21. 302–312.

Coleman, Ben, Elena Casiraghi, Tiffany J. Callahan, et al.. (2024). Association of post-COVID phenotypic manifestations with new-onset psychiatric disease. Translational Psychiatry. 14(1). 246–246. 1 indexed citations

Gliozzo, Jessica, Mauricio Soto, Valentina Guarino, et al.. (2024). Intrinsic-dimension analysis for guiding dimensionality reduction and data fusion in multi-omics data processing. Artificial Intelligence in Medicine. 160. 103049–103049. 6 indexed citations

Cappelletti, Luca, Tommaso Fontana, Elena Casiraghi, et al.. (2024). Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning. Bioinformatics Advances. 4(1). vbae036–vbae036. 4 indexed citations

10.

Valentini, Giorgio, Dario Malchiodi, Jessica Gliozzo, et al.. (2023). The promises of large language models for protein design and modeling. SHILAP Revista de lepidopterología. 3. 1304099–1304099. 18 indexed citations

11.

Ferrè, Laura, Ferdinando Clarelli, Béatrice Pignolet, et al.. (2023). Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach. Journal of Personalized Medicine. 13(1). 122–122. 6 indexed citations

12.

Blau, Hannah, Elena Casiraghi, Johanna Loomba, et al.. (2023). Predictive models of long COVID. EBioMedicine. 96. 104777–104777. 18 indexed citations

13.

Esposito, Andrea, Elena Casiraghi, Elvira Stellato, et al.. (2021). Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System. SHILAP Revista de lepidopterología. Volume 14. 27–39. 4 indexed citations

14.

Ravanmehr, Vida, Hannah Blau, Luca Cappelletti, et al.. (2021). Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer. NAR Genomics and Bioinformatics. 3(4). lqab113–lqab113. 4 indexed citations

15.

Petrini, Alessandro, Marco Mesiti, Max Schubach, et al.. (2020). parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants. GigaScience. 9(5). 9 indexed citations

16.

Casiraghi, Elena, Dario Malchiodi, Gabriella Trucco, et al.. (2020). Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments. IEEE Access. 8. 196299–196325. 56 indexed citations

17.

Galletti, Anna Maria Raspolli, Giorgio Valentini, Valentina De Luise, & Claudia Antonetti. (2011). Conversion of biomass to levulinic acid, a new feedstock for the chemical industry. 112–117. 5 indexed citations

18.

Valentini, Giorgio & Matteo Ré. (2009). Weighted True Path Rule: a multilabel hierarchical algorithm for gene function prediction. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 17 indexed citations

19.

Valentini, Giorgio & Thomas G. Dietterich. (2003). Low bias bagged support vector machines. International Conference on Machine Learning. 752–759. 46 indexed citations

20.

Valentini, Giorgio & Francesco Masulli. (1999). NEURObjects: A Set of Library Classes for Neural Networks Development.. CINECA IRIS Institutial Research Information System (University of Genoa). 3 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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