Philippe Lucarelli

52 total papers · 1.2k total citations
20 papers, 770 citations indexed

About

Philippe Lucarelli is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, Philippe Lucarelli has authored 20 papers receiving a total of 770 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 4 papers in Oncology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Philippe Lucarelli's work include TGF-β signaling in diseases (3 papers), Cell Image Analysis Techniques (3 papers) and Melanoma and MAPK Pathways (3 papers). Philippe Lucarelli is often cited by papers focused on TGF-β signaling in diseases (3 papers), Cell Image Analysis Techniques (3 papers) and Melanoma and MAPK Pathways (3 papers). Philippe Lucarelli collaborates with scholars based in Luxembourg, Germany and United Kingdom. Philippe Lucarelli's co-authors include Thomas Sauter, Mahvash Tavassoli, Nina Raulf, Ivano Amelio, Elham Alsahafi, Katheryn Begg, Ursula Klingmüller, Stacey-Ann Whittaker Brown, Selvam Thavaraj and José M. Vicencio and has published in prestigious journals such as Molecular Cell, Bioinformatics and Cancer Research.

In The Last Decade

Philippe Lucarelli

20 papers receiving 761 citations

Hit Papers

Clinical update on head a... 2019 2026 2021 2023 2019 100 200 300

Author Peers

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

Author Last Decade Papers Cites
Philippe Lucarelli 470 216 190 105 93 20 770
Annett Linge 443 0.9× 318 1.5× 238 1.3× 158 1.5× 83 0.9× 42 884
Laura Lattanzio 332 0.7× 215 1.0× 193 1.0× 125 1.2× 78 0.8× 33 677
Jia‐shun Wu 389 0.8× 246 1.1× 274 1.4× 70 0.7× 141 1.5× 31 787
Fraser L. Baker 307 0.7× 217 1.0× 178 0.9× 120 1.1× 37 0.4× 29 710
Saskia E. Rademakers 299 0.6× 126 0.6× 423 2.2× 154 1.5× 42 0.5× 12 690
Tineke W.H. Meijer 373 0.8× 179 0.8× 383 2.0× 218 2.1× 47 0.5× 27 860
Yukinao Kouzu 540 1.1× 250 1.2× 189 1.0× 109 1.0× 79 0.8× 28 792
Ta Xiao 580 1.2× 174 0.8× 282 1.5× 72 0.7× 135 1.5× 28 827
Jessica M. Molkentine 403 0.9× 269 1.2× 141 0.7× 164 1.6× 26 0.3× 27 710
Jia‐Quan Qu 555 1.2× 205 0.9× 328 1.7× 79 0.8× 68 0.7× 17 752

Countries citing papers authored by Philippe Lucarelli

Since Specialization
Citations

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

Fields of papers citing papers by Philippe Lucarelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philippe Lucarelli

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