Massimo La Rosa

121 total papers · 1.6k total citations
52 papers, 981 citations indexed

About

Massimo La Rosa is a scholar working on Molecular Biology, Cancer Research and Artificial Intelligence. According to data from OpenAlex, Massimo La Rosa has authored 52 papers receiving a total of 981 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 11 papers in Cancer Research and 8 papers in Artificial Intelligence. Recurrent topics in Massimo La Rosa's work include Genomics and Phylogenetic Studies (9 papers), MicroRNA in disease regulation (7 papers) and Scientific Computing and Data Management (6 papers). Massimo La Rosa is often cited by papers focused on Genomics and Phylogenetic Studies (9 papers), MicroRNA in disease regulation (7 papers) and Scientific Computing and Data Management (6 papers). Massimo La Rosa collaborates with scholars based in Italy, India and Spain. Massimo La Rosa's co-authors include Alfonso Urso, Antonino Fiannaca, Riccardo Rizzo, Laura La Paglia, Armando Ialenti, Salvatore Milano, Enrìco Cillari, Viviana Ferlazzo, Gloria Di Bella and P. D’Agostino and has published in prestigious journals such as International Journal of Molecular Sciences, Annals of the New York Academy of Sciences and Expert Systems with Applications.

In The Last Decade

Massimo La Rosa

50 papers receiving 959 citations

Author Peers

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

Author Last Decade Papers Cites
Massimo La Rosa 419 138 128 120 97 52 981
Jianxin Jiang 463 1.1× 215 1.6× 158 1.2× 177 1.5× 95 1.0× 39 1.0k
Jyoti Sharma 657 1.6× 137 1.0× 61 0.5× 146 1.2× 109 1.1× 64 1.1k
Daniel P. Heruth 546 1.3× 130 0.9× 86 0.7× 104 0.9× 42 0.4× 54 1.0k
Xiaolei Hu 293 0.7× 230 1.7× 155 1.2× 67 0.6× 42 0.4× 48 910
Isabelle Leduc 294 0.7× 277 2.0× 71 0.6× 193 1.6× 47 0.5× 37 942
T. M. Mukherjee 396 0.9× 86 0.6× 67 0.5× 171 1.4× 73 0.8× 43 1.1k
Monia Cecati 329 0.8× 68 0.5× 109 0.9× 158 1.3× 114 1.2× 50 932
Hongbin Wang 416 1.0× 154 1.1× 104 0.8× 59 0.5× 124 1.3× 98 1.2k
Ruoyun Tan 475 1.1× 64 0.5× 104 0.8× 73 0.6× 36 0.4× 40 868
Ying Tong 421 1.0× 79 0.6× 156 1.2× 105 0.9× 79 0.8× 67 1000

Countries citing papers authored by Massimo La Rosa

Since Specialization
Citations

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

Fields of papers citing papers by Massimo La Rosa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Massimo La Rosa. 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 Massimo La Rosa. The network helps show where Massimo La Rosa may publish in the future.

Co-authorship network of co-authors of Massimo La Rosa

This figure shows the co-authorship network connecting the top 25 collaborators of Massimo La Rosa. A scholar is included among the top collaborators of Massimo La Rosa 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 Massimo La Rosa. Massimo La Rosa 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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