Matteo Ré

1.8k total citations
28 papers, 453 citations indexed

About

Matteo Ré is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Matteo Ré has authored 28 papers receiving a total of 453 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 5 papers in Computational Theory and Mathematics and 4 papers in Artificial Intelligence. Recurrent topics in Matteo Ré's work include Bioinformatics and Genomic Networks (15 papers), Gene expression and cancer classification (13 papers) and Machine Learning in Bioinformatics (7 papers). Matteo Ré is often cited by papers focused on Bioinformatics and Genomic Networks (15 papers), Gene expression and cancer classification (13 papers) and Machine Learning in Bioinformatics (7 papers). Matteo Ré collaborates with scholars based in Italy, Germany and Sweden. Matteo Ré's co-authors include Giorgio Valentini, Nicolò Cesa‐Bianchi, Marco Mesiti, Peter N. Robinson, Max Schubach, Marco Frasca, Alberto Bertoni, Alberto Paccanaro, Oleg Okun and Alfonso E. Romero and has published in prestigious journals such as Bioinformatics, Scientific Reports and Tetrahedron.

In The Last Decade

Matteo Ré

25 papers receiving 441 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matteo Ré Italy 14 298 121 96 36 28 28 453
Noël Malod‐Dognin United Kingdom 14 543 1.8× 121 1.0× 189 2.0× 32 0.9× 41 1.5× 31 812
Fiona Browne United Kingdom 13 266 0.9× 76 0.6× 76 0.8× 34 0.9× 12 0.4× 39 508
Jiahua Rao China 10 434 1.5× 131 1.1× 256 2.7× 8 0.2× 24 0.9× 24 641
Ladislav Rampášek Canada 6 257 0.9× 71 0.6× 153 1.6× 25 0.7× 22 0.8× 8 532
Vuk Janjić United Kingdom 7 272 0.9× 53 0.4× 96 1.0× 26 0.7× 18 0.6× 10 383
Sarala Wimalaratne United Kingdom 13 609 2.0× 131 1.1× 71 0.7× 42 1.2× 9 0.3× 24 766
Bolin Chen China 13 469 1.6× 78 0.6× 73 0.8× 20 0.6× 12 0.4× 58 613
Tomasz Adamusiak United States 11 425 1.4× 140 1.2× 38 0.4× 116 3.2× 16 0.6× 17 594
Houssam Nassif United States 10 142 0.5× 126 1.0× 37 0.4× 25 0.7× 3 0.1× 22 289
Kenneth Daily United States 10 318 1.1× 60 0.5× 43 0.4× 30 0.8× 6 0.2× 14 507

Countries citing papers authored by Matteo Ré

Since Specialization
Citations

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

Fields of papers citing papers by Matteo Ré

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matteo Ré

This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Ré. A scholar is included among the top collaborators of Matteo Ré 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 Matteo Ré. Matteo Ré 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.
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
2.
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
3.
Schubach, Max, Matteo Ré, Peter N. Robinson, & Giorgio Valentini. (2017). Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants. Scientific Reports. 7(1). 2959–2959. 57 indexed citations
4.
Schubach, Max, Matteo Ré, Peter N. Robinson, & Giorgio Valentini. (2017). Variant relevance prediction in extremely imbalanced training sets. Faculty of 1000 Research Ltd. 6.
5.
Valentini, Giorgio, et al.. (2014). An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods. Artificial Intelligence in Medicine. 61(2). 63–78. 41 indexed citations
6.
Ré, Matteo, Marco Mesiti, & Gianluca Valentini. (2014). On the automated function prediction of big multi-species networks. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 1 indexed citations
7.
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
8.
Ré, Matteo & Giorgio Valentini. (2013). Network-Based Drug Ranking and Repositioning with Respect to DrugBank Therapeutic Categories. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 10(6). 1359–1371. 28 indexed citations
9.
Ré, Matteo, Marco Mesiti, & Giorgio Valentini. (2012). A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(6). 1812–1818. 31 indexed citations
10.
Ré, Matteo & Giorgio Valentini. (2012). Cancer module genes ranking using kernelized score functions. BMC Bioinformatics. 13(S14). S3–S3. 17 indexed citations
11.
Beghini, Alessandro, Francesca Corlazzoli, Luca Del Giacco, et al.. (2012). Regeneration-associated WNT Signaling Is Activated in Long-term Reconstituting AC133bright Acute Myeloid Leukemia Cells. Neoplasia. 14(12). 1236–IN45. 27 indexed citations
12.
Valentini, Giorgio, et al.. (2012). Network integration boosts disease gene prioritization. 1 indexed citations
13.
Cesa‐Bianchi, Nicolò, Matteo Ré, & Giorgio Valentini. (2011). Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference. Machine Learning. 88(1-2). 209–241. 63 indexed citations
14.
Okun, Oleg, Matteo Ré, & Giorgio Valentini. (2011). Ensembles in Machine Learning Applications. Studies in computational intelligence. 23 indexed citations
15.
Ré, Matteo. (2010). Comparing early and late data fusion methods for gene expression prediction. Soft Computing. 15(8). 1497–1504. 4 indexed citations
16.
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
17.
Ré, Matteo, Graziano Pesole, & David S. Horner. (2009). Accurate discrimination of conserved coding and non-coding regions through multiple indicators of evolutionary dynamics. BMC Bioinformatics. 10(1). 282–282. 4 indexed citations
18.
Ré, Matteo & Giorgio Valentini. (2009). Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 95–104. 8 indexed citations
19.
Ré, Matteo & Giulio Pavesi. (2008). Detecting conserved coding genomic regions through signal processing of nucleotide substitution patterns. Artificial Intelligence in Medicine. 45(2-3). 117–123. 1 indexed citations
20.
Ré, Matteo, Flavio Mignone, Michele Iacono, et al.. (2005). A new strategy to identify novel genes and gene isoforms: Analysis of human chromosomes 15, 21 and 22. Gene. 365. 35–40. 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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