Andrea Passerini

242 total papers · 4.1k total citations
99 papers, 2.0k citations indexed

About

Andrea Passerini is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Andrea Passerini has authored 99 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Artificial Intelligence, 27 papers in Molecular Biology and 17 papers in Computational Theory and Mathematics. Recurrent topics in Andrea Passerini's work include Machine Learning and Algorithms (16 papers), Bayesian Modeling and Causal Inference (13 papers) and Machine Learning in Bioinformatics (13 papers). Andrea Passerini is often cited by papers focused on Machine Learning and Algorithms (16 papers), Bayesian Modeling and Causal Inference (13 papers) and Machine Learning in Bioinformatics (13 papers). Andrea Passerini collaborates with scholars based in Italy, Belgium and Germany. Andrea Passerini's co-authors include Paolo Frasconi, Alessio Ceroni, Alessandro Vullo, Guy Van den Broeck, Vaishak Belle, Roberto Battiti, Stefano Teso, Massimiliano Pontil, Marco Lippi and Luc De Raedt and has published in prestigious journals such as Nucleic Acids Research, The Journal of Cell Biology and Bioinformatics.

In The Last Decade

Andrea Passerini

93 papers receiving 1.9k citations

Hit Papers

Machine learning for micr... 2023 2026 2024 2023 25 50 75 100

Author Peers

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

Author Last Decade Papers Cites
Andrea Passerini 734 672 262 167 161 99 2.0k
Celine Vens 565 0.8× 831 1.2× 252 1.0× 227 1.4× 270 1.7× 73 1.8k
Francisco B. Pereira 618 0.8× 667 1.0× 151 0.6× 316 1.9× 95 0.6× 80 2.4k
Giorgio Valentini 1.1k 1.5× 822 1.2× 212 0.8× 350 2.1× 113 0.7× 126 2.6k
Satwinder Singh 938 1.3× 338 0.5× 483 1.8× 178 1.1× 372 2.3× 97 2.8k
Évelyne Lutton 365 0.5× 412 0.6× 215 0.8× 311 1.9× 55 0.3× 145 2.8k
Pooja Yadav 375 0.5× 317 0.5× 106 0.4× 145 0.9× 108 0.7× 69 2.2k
Jerry Li 874 1.2× 768 1.1× 283 1.1× 114 0.7× 64 0.4× 104 2.9k
Raghvendra Mall 977 1.3× 430 0.6× 176 0.7× 163 1.0× 286 1.8× 119 2.7k
David Gilbert 2.0k 2.8× 445 0.7× 368 1.4× 99 0.6× 113 0.7× 98 2.9k
Jing Lu 662 0.9× 478 0.7× 408 1.6× 155 0.9× 124 0.8× 60 1.9k

Countries citing papers authored by Andrea Passerini

Since Specialization
Citations

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

Fields of papers citing papers by Andrea Passerini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrea Passerini

This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Passerini. A scholar is included among the top collaborators of Andrea Passerini 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 Andrea Passerini. Andrea Passerini 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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