Massimo La Rosa

1.6k total citations
52 papers, 986 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 986 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 Antonino Fiannaca, Alfonso Urso, Riccardo Rizzo, Laura La Paglia, Armando Ialenti, Enrìco Cillari, Viviana Ferlazzo, Salvatore Milano, 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 964 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Massimo La Rosa Italy 17 421 138 128 120 97 52 986
Xiuxia Qu China 21 747 1.8× 172 1.2× 97 0.8× 112 0.9× 71 0.7× 28 1.7k
Claudia Rangel‐Escareño Mexico 20 642 1.5× 119 0.9× 143 1.1× 208 1.7× 184 1.9× 68 1.4k
Arif Uddin India 20 1.0k 2.5× 132 1.0× 204 1.6× 160 1.3× 52 0.5× 68 1.7k
Monia Cecati Italy 18 336 0.8× 69 0.5× 111 0.9× 158 1.3× 114 1.2× 50 948
Wenji Wang China 20 401 1.0× 84 0.6× 168 1.3× 304 2.5× 47 0.5× 100 1.3k
Guido Sauer Germany 16 869 2.1× 96 0.7× 75 0.6× 87 0.7× 118 1.2× 21 1.5k
Jason P. Smith United States 19 544 1.3× 118 0.9× 130 1.0× 54 0.5× 58 0.6× 46 1.0k
Casey P. Shannon Canada 13 633 1.5× 87 0.6× 112 0.9× 147 1.2× 64 0.7× 38 1.5k
Scott Tighe United States 19 649 1.5× 66 0.5× 128 1.0× 80 0.7× 92 0.9× 53 1.3k
Vicente Arnau Spain 12 564 1.3× 78 0.6× 82 0.6× 147 1.2× 55 0.6× 37 1.0k

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

20 of 20 papers shown
1.
2.
Brancato, Valentina, Nadia Brancati, Massimo La Rosa, et al.. (2023). A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer. Sensors. 23(3). 1552–1552. 3 indexed citations
3.
Fiannaca, Antonino, Massimo La Rosa, Laura La Paglia, Salvatore Gaglio, & Alfonso Urso. (2023). GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data. Briefings in Bioinformatics. 24(6). 2 indexed citations
4.
5.
Fiannaca, Antonino, Laura La Paglia, Massimo La Rosa, Riccardo Rizzo, & Alfonso Urso. (2020). miRTissue ce: extending miRTissue web service with the analysis of ceRNA-ceRNA interactions. BMC Bioinformatics. 21(S8). 199–199. 13 indexed citations
6.
Boscaino, V., Antonino Fiannaca, Laura La Paglia, et al.. (2019). MiRNA therapeutics based on logic circuits of biological pathways. BMC Bioinformatics. 20(S9). 344–344. 14 indexed citations
7.
Fiannaca, Antonino, Massimo La Rosa, Laura La Paglia, & Alfonso Urso. (2018). miRTissue: a web application for the analysis of miRNA-target interactions in human tissues. BMC Bioinformatics. 19(S15). 434–434. 6 indexed citations
8.
Fiannaca, Antonino, Laura La Paglia, Massimo La Rosa, et al.. (2018). Deep learning models for bacteria taxonomic classification of metagenomic data. BMC Bioinformatics. 19(S7). 198–198. 81 indexed citations
9.
Fiannaca, Antonino, Massimo La Rosa, Laura La Paglia, Riccardo Rizzo, & Alfonso Urso. (2017). nRC: non-coding RNA Classifier based on structural features. BioData Mining. 10(1). 27–27. 52 indexed citations
10.
Fiannaca, Antonino, Massimo La Rosa, Laura La Paglia, Riccardo Rizzo, & Alfonso Urso. (2016). MiRNATIP: a SOM-based miRNA-target interactions predictor. BMC Bioinformatics. 17(S11). 321–321. 10 indexed citations
11.
Fiannaca, Antonino, Massimo La Rosa, Alfonso Urso, Riccardo Rizzo, & Salvatore Gaglio. (2013). A knowledge-based decision support system in bioinformatics: an application to protein complex extraction. BMC Bioinformatics. 14(S1). S5–S5. 14 indexed citations
12.
Rosa, Massimo La, Antonino Fiannaca, Riccardo Rizzo, & Alfonso Urso. (2013). Alignment-free analysis of barcode sequences by means of compression-based methods. BMC Bioinformatics. 14(S7). S4–S4. 16 indexed citations
13.
Fiannaca, Antonino, et al.. (2011). An Intelligent System for Decision Support in Bioinformatics.. Nova Science Publishers (Nova Science Publishers, Inc.). 2011. 35–36. 5 indexed citations
14.
Fabbiano, Francesco, Valentina Rizzo, Giuseppe Cammarata, et al.. (2007). Loss of heterozygosity in acute leukemia: evidence of frequent submicroscopic deletions. Haematologica. 92(5). 678–681. 1 indexed citations
15.
Kerber, F., Don J. Lindler, Paul Bristow, et al.. (2005). Ageing of Spectral Lamps in Space. 39. 4. 1 indexed citations
16.
Vitale, G., S Mansueto, Salvatore Di Rosa, et al.. (2001). The Acute Phase Response in Sicilian Patients with Boutonneuse Fever Admitted to Hospitals in Palermo, 1992–1997. Journal of Infection. 42(1). 33–39. 24 indexed citations
17.
Ialenti, Armando, Angela Ianaro, Pasquale Maffia, et al.. (2001). Role of nuclear factor-κB in a rat model of vascular injury. Naunyn-Schmiedeberg s Archives of Pharmacology. 364(4). 343–350. 9 indexed citations
18.
Buiatti, Eva, Emanuele Crocetti, Lorenzo Gafà, et al.. (1997). Incidence of second primary cancers in three Italian population-based cancer registries. European Journal of Cancer. 33(11). 1829–1834. 73 indexed citations
19.
Sconzo, G, Massimo La Rosa, Marcelo Farina, et al.. (1988). Isolation and characterization of a sea urchin hsp 70 gene segment. Cell Differentiation. 24(2). 97–104. 5 indexed citations
20.
Roccheri, Maria Carmela, et al.. (1988). Stress proteins by zinc ions in sea urchin embryos. Cell Differentiation. 24(3). 209–213. 17 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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