Federico De Masi

62 total papers · 3.7k total citations
18 papers, 1.5k citations indexed

About

Federico De Masi is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Genetics. According to data from OpenAlex, Federico De Masi has authored 18 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 3 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Genetics. Recurrent topics in Federico De Masi's work include Machine Learning in Bioinformatics (3 papers), Bioinformatics and Genomic Networks (3 papers) and Gene Regulatory Network Analysis (2 papers). Federico De Masi is often cited by papers focused on Machine Learning in Bioinformatics (3 papers), Bioinformatics and Genomic Networks (3 papers) and Gene Regulatory Network Analysis (2 papers). Federico De Masi collaborates with scholars based in Denmark, United States and Germany. Federico De Masi's co-authors include Martha L. Bulyk, Daniel E. Newburger, Albertha J.M. Walhout, Christian A Grove, Rune Linding, Toby J. Gibson, Alexander Stark, Luís Serrano, Joe Lewis and M. Inmaculada Barrasa and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Federico De Masi

16 papers receiving 1.5k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Federico De Masi 1.2k 312 132 111 96 18 1.5k
Michael A. Shogren‐Knaak 2.2k 1.8× 245 0.8× 151 1.1× 53 0.5× 20 0.2× 27 2.3k
Thomas J. Lindell 1.1k 0.9× 163 0.5× 218 1.7× 38 0.3× 33 0.3× 30 1.6k
Paul S. Kayne 2.0k 1.7× 325 1.0× 246 1.9× 31 0.3× 68 0.7× 26 2.3k
Randall F. Smith 1.0k 0.9× 291 0.9× 256 1.9× 35 0.3× 50 0.5× 23 1.8k
Richard S. Rogers 1.8k 1.5× 139 0.4× 140 1.1× 181 1.6× 32 0.3× 32 2.1k
Michael F. Chou 1.3k 1.1× 169 0.5× 152 1.2× 31 0.3× 52 0.5× 21 1.7k
Sanié Mnaimneh 1.5k 1.3× 197 0.6× 209 1.6× 18 0.2× 73 0.8× 18 2.0k
Sean J. McIlwain 1.4k 1.2× 335 1.1× 158 1.2× 38 0.3× 30 0.3× 43 1.9k
Philippe Vaglio 1.7k 1.5× 152 0.5× 162 1.2× 34 0.3× 311 3.2× 22 2.0k
Michael R. Meyer 2.0k 1.7× 226 0.7× 155 1.2× 39 0.4× 26 0.3× 12 2.3k

Countries citing papers authored by Federico De Masi

Since Specialization
Citations

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

Fields of papers citing papers by Federico De Masi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Federico De Masi. 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 Federico De Masi. The network helps show where Federico De Masi may publish in the future.

Co-authorship network of co-authors of Federico De Masi

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

All Works

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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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