Andrea Malossini
- Plant Science top 10%
- Molecular Biology
- Artificial Intelligence top 10%
- Food Science top 10%
- Electrical and Electronic Engineering
- Co-authors
- Enrico BlanzieriTommaso CalarcoMichele PerazzolliLorenzo DemattèRiccardo VelascoRaymond T. NgAlessandro CestaroRoberto Viola
- Topics
- Gene expression and cancer classification (4 papers)Machine Learning in Bioinformatics (3 papers)Neural Networks and Reservoir Computing (2 papers)
- Partner nations
- ItalyCanadaUnited States
In The Last Decade
Andrea Malossini
8 papers receiving 391 citations
Peers
Comparison fields: 5 of 81
- Plant Science 188
- Molecular Biology 168
- Artificial Intelligence 116
- Food Science 103
- Electrical and Electronic Engineering 32
Countries citing papers authored by Andrea Malossini
This map shows the geographic impact of Andrea Malossini'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 Malossini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrea Malossini more than expected).
Fields of papers citing papers by Andrea Malossini
This network shows the impact of papers produced by Andrea Malossini. 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 Malossini. The network helps show where Andrea Malossini may publish in the future.
Co-authorship network of co-authors of Andrea Malossini
This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Malossini. A scholar is included among the top collaborators of Andrea Malossini 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 Malossini. Andrea Malossini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 19 | |
| 2 | Kernel Integration using von Neumann Entropy | 1 |
| 3 | 108 | |
| 4 | 197 | |
| 5 | 53 | |
| 6 | Validation of CFS classification with different data sources | 0 |
| 7 | Simple Methods for Peak Detection in Time Series Microarray Data. | 9 |
| 8 | Assessment of SVM Reliability for Microarray Data Analysis | 7 |
| 9 | QGA: a Quantum Genetic Algorithm | 7 |
About Andrea Malossini
Andrea Malossini is a scholar working on Biophysics, Artificial Intelligence and Molecular Biology, having authored 9 papers that have together received 401 indexed citations. Recurring topics across this work include Gene expression and cancer classification (4 papers), Machine Learning in Bioinformatics (3 papers) and Neural Networks and Reservoir Computing (2 papers). The work is most often cited by research in Food Science (103 citations), Plant Science (188 citations) and Tourism, Leisure and Hospitality Management (6 citations). Andrea Malossini has collaborated with scholars based in Italy, Canada and United States. Frequent co-authors include Enrico Blanzieri, Tommaso Calarco, Michele Perazzolli, Lorenzo Demattè, Riccardo Velasco, Raymond T. Ng, Alessandro Cestaro, Roberto Viola, Paolo Fontana and S. Pilati. Their work appears in journals such as Bioinformatics, IEEE Transactions on Evolutionary Computation and BMC Genomics.
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.