Daniel Veltri

1.6k total citations · 1 hit paper
23 papers, 934 citations indexed

About

Daniel Veltri is a scholar working on Molecular Biology, Plant Science and Cell Biology. According to data from OpenAlex, Daniel Veltri has authored 23 papers receiving a total of 934 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 7 papers in Plant Science and 6 papers in Cell Biology. Recurrent topics in Daniel Veltri's work include Antimicrobial Peptides and Activities (6 papers), Plant Pathogens and Fungal Diseases (6 papers) and Biochemical and Structural Characterization (5 papers). Daniel Veltri is often cited by papers focused on Antimicrobial Peptides and Activities (6 papers), Plant Pathogens and Fungal Diseases (6 papers) and Biochemical and Structural Characterization (5 papers). Daniel Veltri collaborates with scholars based in United States, United Arab Emirates and Australia. Daniel Veltri's co-authors include Amarda Shehu, Uday Kamath, Jo Anne Crouch, Nadine Kabbani, Jacob C. Nordman, Martha Malapi‐Wight, Eric O. Long, Xiaoxuan Zhuang, Catalina Salgado‐Salazar and Jill E. Demers and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Daniel Veltri

21 papers receiving 922 citations

Hit Papers

Deep learning improves antimicrobial peptide recognition 2018 2026 2020 2023 2018 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep learning improves antimicrobial peptide recognition
    2018 367 citations Daniel Veltri, Uday Kamath et al. Bioinformatics profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Veltri United States 15 699 316 127 100 71 23 934
Martina Leipelt Germany 10 689 1.0× 228 0.7× 175 1.4× 66 0.7× 116 1.6× 13 877
Pallob Kundu India 17 474 0.7× 94 0.3× 232 1.8× 42 0.4× 62 0.9× 29 779
Jean‐François Chich France 18 586 0.8× 88 0.3× 83 0.7× 48 0.5× 71 1.0× 32 877
Kil Lyong Kim South Korea 14 413 0.6× 380 1.2× 65 0.5× 25 0.3× 130 1.8× 37 700
Dilmurat Yusuf Germany 12 525 0.8× 70 0.2× 65 0.5× 24 0.2× 52 0.7× 15 793
Julianne H. Grose United States 20 722 1.0× 164 0.5× 281 2.2× 58 0.6× 33 0.5× 48 1.2k
Kyung‐Baeg Roh South Korea 17 420 0.6× 99 0.3× 88 0.7× 70 0.7× 606 8.5× 34 1.2k
María José Sánchez-Barrena Spain 15 670 1.0× 57 0.2× 546 4.3× 73 0.7× 42 0.6× 25 1.1k
Xiaoxia Xu China 19 506 0.7× 280 0.9× 244 1.9× 8 0.1× 325 4.6× 55 924
Carlos Muñóz-Garay Mexico 25 1.5k 2.1× 113 0.4× 625 4.9× 17 0.2× 97 1.4× 51 1.8k

Countries citing papers authored by Daniel Veltri

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Daniel Veltri

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Veltri. A scholar is included among the top collaborators of Daniel Veltri 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 Daniel Veltri. Daniel Veltri 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.
Yan, Shankai, Ling Luo, Po‐Ting Lai, et al.. (2022). PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology. Journal of Biomedical Informatics. 129. 104059–104059. 4 indexed citations
2.
Luo, Ling, Shankai Yan, Po‐Ting Lai, et al.. (2021). PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology. Bioinformatics. 37(13). 1884–1890. 37 indexed citations
3.
4.
Singh, Kavita, Martin Burkhardt, Raúl Herrera, et al.. (2020). Structure and function of a malaria transmission blocking vaccine targeting Pfs230 and Pfs230-Pfs48/45 proteins. Communications Biology. 3(1). 395–395. 36 indexed citations
5.
Zhuang, Xiaoxuan, Daniel Veltri, & Eric O. Long. (2019). Genome-Wide CRISPR Screen Reveals Cancer Cell Resistance to NK Cells Induced by NK-Derived IFN-γ. Frontiers in Immunology. 10. 2879–2879. 41 indexed citations
6.
Malapi‐Wight, Martha, Daniel Veltri, Kurt Heungens, et al.. (2019). Global distribution of mating types shows limited opportunities for mating across populations of fungi causing boxwood blight disease. Fungal Genetics and Biology. 131. 103246–103246. 16 indexed citations
7.
Zhang, Ning, Guohong Cai, Dana C. Price, et al.. (2018). Genome wide analysis of the transition to pathogenic lifestyles in Magnaporthales fungi. Scientific Reports. 8(1). 5862–5862. 21 indexed citations
8.
Rivera, Yazmín, Catalina Salgado‐Salazar, Daniel Veltri, Martha Malapi‐Wight, & Jo Anne Crouch. (2018). Genome analysis of the ubiquitous boxwood pathogenPseudonectria foliicola. PeerJ. 6. e5401–e5401. 17 indexed citations
9.
Veltri, Daniel, Uday Kamath, & Amarda Shehu. (2018). Deep learning improves antimicrobial peptide recognition. Bioinformatics. 34(16). 2740–2747. 367 indexed citations breakdown →
10.
Veltri, Daniel, Zhiwen Li, Sandhya Xirasagar, et al.. (2018). Balancing Confidentiality and Sharing of Genomic and Phenotypic Data in a Clinical Research System. 533–533. 1 indexed citations
11.
Veltri, Daniel, et al.. (2016). SimpleSynteny: a web-based tool for visualization of microsynteny across multiple species. Nucleic Acids Research. 44(W1). W41–W45. 83 indexed citations
12.
Malapi‐Wight, Martha, Jill E. Demers, Daniel Veltri, Robert E. Marra, & Jo Anne Crouch. (2016). LAMP Detection Assays for Boxwood Blight Pathogens: A Comparative Genomics Approach. Scientific Reports. 6(1). 26140–26140. 30 indexed citations
13.
Salgado‐Salazar, Catalina, Yazmín Rivera, Daniel Veltri, & Jo Anne Crouch. (2015). Polymorphic SSR markers for Plasmopara obducens (Peronosporaceae), the newly emergent downy mildew pathogen of Impatiens (Balsaminaceae). Applications in Plant Sciences. 3(11). 5 indexed citations
14.
Veltri, Daniel, Uday Kamath, & Amarda Shehu. (2015). Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 14(2). 300–313. 49 indexed citations
15.
Nordman, Jacob C., Daniel Veltri, Kun Yang, et al.. (2013). Menthol Inhibits 5-HT3 Receptor–Mediated Currents.
16.
Kabbani, Nadine, Jacob C. Nordman, Daniel Veltri, et al.. (2013). Are nicotinic acetylcholine receptors coupled to G proteins?. BioEssays. 35(12). 1025–1034. 72 indexed citations
17.
Nordman, Jacob C., Daniel Veltri, Keun‐Hang Susan Yang, et al.. (2013). Menthol Binding and Inhibition of α7-Nicotinic Acetylcholine Receptors. PLoS ONE. 8(7). e67674–e67674. 65 indexed citations
18.
Nordman, Jacob C., Daniel Veltri, Keun‐Hang Susan Yang, et al.. (2013). Menthol Inhibits 5-HT3 Receptor–Mediated Currents. Journal of Pharmacology and Experimental Therapeutics. 347(2). 398–409. 40 indexed citations
19.
Veltri, Daniel, et al.. (2013). Binary Response Models for Recognition of Antimicrobial Peptides. 76–85. 15 indexed citations
20.

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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