Marco Frasca

1.2k total citations
31 papers, 369 citations indexed

About

Marco Frasca is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Marco Frasca has authored 31 papers receiving a total of 369 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 6 papers in Artificial Intelligence. Recurrent topics in Marco Frasca's work include Bioinformatics and Genomic Networks (19 papers), Gene expression and cancer classification (11 papers) and Machine Learning in Bioinformatics (9 papers). Marco Frasca is often cited by papers focused on Bioinformatics and Genomic Networks (19 papers), Gene expression and cancer classification (11 papers) and Machine Learning in Bioinformatics (9 papers). Marco Frasca collaborates with scholars based in Italy, United States and United Kingdom. Marco Frasca's co-authors include Giorgio Valentini, Dario Malchiodi, Alessandro Petrini, Matteo Ré, Alberto Bertoni, Marco Mesiti, Elena Casiraghi, Nicolò Cesa‐Bianchi, Peter N. Robinson and Giulio Pavesi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Marco Frasca

30 papers receiving 364 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Frasca Italy 11 186 102 55 51 42 31 369
Edward Kien Yee Yapp Singapore 9 363 2.0× 81 0.8× 50 0.9× 69 1.4× 45 1.1× 10 585
Quang‐Thai Ho Taiwan 14 544 2.9× 131 1.3× 49 0.9× 45 0.9× 47 1.1× 18 794
Zerrin Işık Türkiye 10 150 0.8× 135 1.3× 69 1.3× 71 1.4× 120 2.9× 32 410
Xuequn Shang China 10 225 1.2× 130 1.3× 29 0.5× 33 0.6× 28 0.7× 51 398
Hailin Hu China 14 546 2.9× 84 0.8× 84 1.5× 48 0.9× 54 1.3× 23 747
Hyunjin Kim South Korea 12 121 0.7× 90 0.9× 16 0.3× 34 0.7× 25 0.6× 24 338
Matteo Ré Italy 14 298 1.6× 121 1.2× 96 1.7× 13 0.3× 28 0.7× 28 453
Trinh‐Trung‐Duong Nguyen Taiwan 13 379 2.0× 90 0.9× 52 0.9× 22 0.4× 15 0.4× 20 520
Huang Cheng-bing China 4 192 1.0× 56 0.5× 48 0.9× 19 0.4× 33 0.8× 10 369
David B. Blumenthal Germany 13 218 1.2× 169 1.7× 112 2.0× 17 0.3× 105 2.5× 48 544

Countries citing papers authored by Marco Frasca

Since Specialization
Citations

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

Fields of papers citing papers by Marco Frasca

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Frasca

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Frasca. A scholar is included among the top collaborators of Marco Frasca 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 Marco Frasca. Marco Frasca 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.
Ferragina, Paolo, et al.. (2023). On Nonlinear Learned String Indexing. IEEE Access. 11. 74021–74034. 6 indexed citations
2.
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
3.
Frasca, Marco, et al.. (2022). Identification of key miRNAs in prostate cancer progression based on miRNA-mRNA network construction. Computational and Structural Biotechnology Journal. 20. 864–873. 7 indexed citations
4.
5.
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
6.
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
7.
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
8.
Perlasca, Paolo, Marco Frasca, Jessica Gliozzo, et al.. (2020). Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools. PLoS ONE. 15(12). e0244241–e0244241. 6 indexed citations
9.
Gliozzo, Jessica, Paolo Perlasca, Marco Mesiti, et al.. (2020). Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction. Scientific Reports. 10(1). 3612–3612. 7 indexed citations
10.
Perlasca, Paolo, Marco Frasca, Marco Notaro, et al.. (2019). UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction. BMC Bioinformatics. 20(1). 422–422. 6 indexed citations
11.
Frasca, Marco & Nicolò Cesa‐Bianchi. (2018). Combining Cost-Sensitive Classification with Negative Selection for Protein Function Prediction.. arXiv (Cornell University). 1 indexed citations
12.
Frasca, Marco, Giuliano Grossi, Jessica Gliozzo, et al.. (2018). A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks. BMC Bioinformatics. 19(S10). 353–353. 1 indexed citations
13.
Casiraghi, Elena, Veronica Huber, Marco Frasca, et al.. (2018). A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections. BMC Bioinformatics. 19(S10). 357–357. 15 indexed citations
14.
Frasca, Marco & Dario Malchiodi. (2017). Exploiting Negative Sample Selection for Prioritizing Candidate Disease Genes. 3(3). 47–47. 2 indexed citations
15.
Frasca, Marco. (2017). Gene2DisCo: Gene to disease using disease commonalities. Artificial Intelligence in Medicine. 82. 34–46. 5 indexed citations
16.
Frasca, Marco & Nicolò Cesa‐Bianchi. (2017). Multitask Protein Function Prediction through Task Dissimilarity. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 16(5). 1550–1560. 12 indexed citations
17.
Frasca, Marco, Alberto Bertoni, & Giorgio Valentini. (2015). UNIPred: Unbalance-Aware Network Integration and Prediction of Protein Functions. Journal of Computational Biology. 22(12). 1057–1074. 11 indexed citations
18.
Frasca, Marco, Alberto Bertoni, Matteo Ré, & Giorgio Valentini. (2013). A neural network algorithm for semi-supervised node label learning from unbalanced data. Neural Networks. 43. 84–98. 38 indexed citations
19.
Frasca, Marco, Andrea Bertoni, & Giorgio Valentini. (2013). An unbalance-aware network integration method for gene function prediction. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano).
20.
Muselli, Marco, et al.. (2010). A Mathematical Model for the Validation of Gene Selection Methods. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8(5). 1385–1392. 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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