Francesco Pappalardo
- Modeling and Simulation top 1%
- Mathematical Biology Tumor Growth 11
- Health Informatics top 5%
- Immunology top 5%
- Immunotherapy and Immune Responses 20
- T-cell and B-cell Immunology 13
- Molecular Biology top 10%
- vaccines and immunoinformatics approaches 34
- Gene Regulatory Network Analysis 24
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- Monoclonal and Polyclonal Antibodies Research 17
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- Multiple Sclerosis Research Studies 16
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- Computational Drug Discovery Methods 10
- Co-authors
- Santo MottaMarzio PennisiGiulia RussoMarco VicecontiFlora T. MusuambaPier‐Luigi LolliniFilippo CastiglioneFerdınando Chıacchıo
- Journals
- BMC Bioinformatics (16 papers)Bioinformatics (8 papers)Briefings in Bioinformatics (6 papers)
- Partner nations
- ItalyUnited KingdomUnited States
In The Last Decade
Francesco Pappalardo
120 papers receiving 2.5k citations
Peers
Comparison fields: 5 of 178
- Modeling and Simulation 342
- Health Informatics 36
- Immunology 468
- Molecular Biology 1.1k
- Radiology, Nuclear Medicine and Imaging 309
Countries citing papers authored by Francesco Pappalardo
This map shows the geographic impact of Francesco Pappalardo'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 Francesco Pappalardo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco Pappalardo more than expected).
Fields of papers citing papers by Francesco Pappalardo
This network shows the impact of papers produced by Francesco Pappalardo. 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 Francesco Pappalardo. The network helps show where Francesco Pappalardo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Francesco Pappalardo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 5 | |
| 3 | 2024 | 2 | |
| 4 | 2022 | 39 | |
| 5 | 2022 | 7 | |
| 6 | 2022 | 1 | |
| 7 | 2021 | 23 | |
| 8 | 2020 | 33 | |
| 9 | 2020 | 11 | |
| 10 | 2020 | 202 | |
| 11 | 2019 | 18 | |
| 12 | 2016 | 10 | |
| 13 | 2015 | 19 | |
| 14 | 2014 | 7 | |
| 15 | 2012 | 36 | |
| 16 | 2010 | 69 | |
| 17 | Modeling artificial immunity against mammary carcinoma | 2010 | 4 |
| 18 | 2009 | 6 | |
| 19 | 2006 | 49 | |
| 20 | 2005 | 22 |
About Francesco Pappalardo
Francesco Pappalardo is a scholar working on Modeling and Simulation, Immunology and Molecular Biology, having authored 128 papers that have together received 2.6k indexed citations. Recurring topics across this work include vaccines and immunoinformatics approaches (34 papers), Gene Regulatory Network Analysis (24 papers), Immunotherapy and Immune Responses (20 papers), Monoclonal and Polyclonal Antibodies Research (17 papers), Multiple Sclerosis Research Studies (16 papers), T-cell and B-cell Immunology (13 papers), Mathematical Biology Tumor Growth (11 papers) and Computational Drug Discovery Methods (10 papers). The work is most often cited by research in Modeling and Simulation (342 citations), Health Informatics (36 citations) and Immunology (468 citations). Francesco Pappalardo has collaborated with scholars based in Italy, United Kingdom and United States. Frequent co-authors include Santo Motta, Marzio Pennisi, Giulia Russo, Marco Viceconti, Flora T. Musuamba, Pier‐Luigi Lollini, Filippo Castiglione, Ferdınando Chıacchıo, Blanca Rodríguez and Carlo Bianca. Their work appears in journals such as BMC Bioinformatics, Bioinformatics, Briefings in Bioinformatics, Computational and Structural Biotechnology Journal and BioMed Research International.
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.