Daniel Veltri
Impact in
- Microbiology top 1%
- Antimicrobial Peptides and Activities
- Cell Biology top 10%
- Plant Pathogens and Fungal Diseases
Papers in
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- Antimicrobial Peptides and Activities 6
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- Plant Pathogens and Fungal Diseases 6
- Co-authors
- Amarda Shehu (8 shared papers)Uday Kamath (3 shared papers)Jo Anne Crouch (7 shared papers)Martha Malapi‐Wight (5 shared papers)Nadine Kabbani (3 shared papers)Jacob C. Nordman (3 shared papers)Xiaoxuan Zhuang (1 shared paper)Eric O. Long (1 shared paper)
- Journals
- Scientific Reports (2 papers)Frontiers in Immunology (2 papers)Bioinformatics (2 papers)Journal of Allergy and Clinical Immunology (2 papers)BioEssays (1 paper)
- Partner nations
- United StatesAustraliaUnited Arab Emirates
In The Last Decade
Daniel Veltri
26 papers receiving 1.3k citations
Daniel Veltri's Hit Papers
Peers
Comparison fields: 5 of 84
- Microbiology 350
- Cell Biology 134
- Immunology 174
- Molecular Biology 341
- Computational Theory and Mathematics 71
Countries citing papers authored by Daniel Veltri
This map shows the geographic impact of Daniel Veltri'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 Daniel Veltri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Veltri more than expected).
Fields of papers citing papers by Daniel Veltri
This network shows the impact of papers produced by Daniel Veltri. 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 Daniel Veltri. The network helps show where Daniel Veltri may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Veltri, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep learning improves antimicrobial peptide recognition Hit paper breakdown → | 2018 | 437 |
| 2 | 2018 | 122 | |
| 3 | 2016 | 90 | |
| 4 | 2013 | 75 | |
| 5 | 2013 | 68 | |
| 6 | 2022 | 53 | |
| 7 | 2015 | 49 | |
| 8 | 2019 | 48 | |
| 9 | 2015 | 46 | |
| 10 | 2020 | 46 | |
| 11 | 2021 | 45 | |
| 12 | 2013 | 44 | |
| 13 | 2016 | 31 | |
| 14 | 2015 | 25 | |
| 15 | 2018 | 22 | |
| 16 | 2018 | 17 | |
| 17 | 2019 | 17 | |
| 18 | 2013 | 15 | |
| 19 | 2015 | 7 | |
| 20 | 2013 | 5 |
About Daniel Veltri
Daniel Veltri is a scholar working on Microbiology, Cell Biology, Immunology, Molecular Biology and Genetics, having authored 26 papers that have together received 1.3k indexed citations. Recurring topics across this work include Antimicrobial Peptides and Activities (6 papers), Plant Pathogens and Fungal Diseases (6 papers), Immunodeficiency and Autoimmune Disorders (3 papers), Biomedical Text Mining and Ontologies (2 papers), Nicotinic Acetylcholine Receptors Study (2 papers), Genomics and Rare Diseases (2 papers), Receptor Mechanisms and Signaling (1 paper) and Electronic Health Records Systems (1 paper). The work is most often cited by research in Microbiology (350 citations), Cell Biology (134 citations), Immunology (174 citations), Molecular Biology (341 citations) and Computational Theory and Mathematics (71 citations). Daniel Veltri has collaborated with scholars based in United States, Australia and United Arab Emirates. Frequent co-authors include Amarda Shehu, Uday Kamath, Jo Anne Crouch, Martha Malapi‐Wight, Nadine Kabbani, Jacob C. Nordman, Xiaoxuan Zhuang, Eric O. Long, Catalina Salgado‐Salazar and Jill E. Demers. Their work appears in journals such as Scientific Reports, Frontiers in Immunology, Bioinformatics, Journal of Allergy and Clinical Immunology and BioEssays.
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.