Sara D’Angelo

1.2k total citations
36 papers, 784 citations indexed

About

Sara D’Angelo is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Immunology. According to data from OpenAlex, Sara D’Angelo has authored 36 papers receiving a total of 784 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 27 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Immunology. Recurrent topics in Sara D’Angelo's work include Monoclonal and Polyclonal Antibodies Research (27 papers), Glycosylation and Glycoproteins Research (13 papers) and Protein purification and stability (10 papers). Sara D’Angelo is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (27 papers), Glycosylation and Glycoproteins Research (13 papers) and Protein purification and stability (10 papers). Sara D’Angelo collaborates with scholars based in United States, Italy and United Kingdom. Sara D’Angelo's co-authors include Andrew Bradbury, Fortunato Ferrara, Leslie Naranjo, Csaba Kiss, Daniele Sblattero, Richard L. Sidman, Claudio Santoro, Renata Pasqualini, Wadih Arap and Peter Hraber and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and PLoS ONE.

In The Last Decade

Sara D’Angelo

34 papers receiving 754 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sara D’Angelo United States 16 553 432 132 95 89 36 784
Martina L. Jones Australia 15 454 0.8× 348 0.8× 98 0.7× 127 1.3× 94 1.1× 39 786
Gholamreza Hassanzadeh‐Ghassabeh Belgium 18 668 1.2× 621 1.4× 273 2.1× 93 1.0× 30 0.3× 25 1.1k
Rolle Rahikainen Finland 12 375 0.7× 142 0.3× 61 0.5× 122 1.3× 61 0.7× 20 696
M.R. Suresh Canada 18 551 1.0× 500 1.2× 228 1.7× 64 0.7× 50 0.6× 44 981
Mariela Urrutia Argentina 12 453 0.8× 428 1.0× 225 1.7× 40 0.4× 21 0.2× 18 798
John Steven United Kingdom 14 334 0.6× 280 0.6× 135 1.0× 54 0.6× 36 0.4× 23 547
Vanina Alzogaray Argentina 8 357 0.6× 405 0.9× 207 1.6× 38 0.4× 19 0.2× 9 649
Mikaela Friedman Sweden 14 461 0.8× 496 1.1× 78 0.6× 71 0.7× 32 0.4× 24 800
Yufei Xiang United States 15 815 1.5× 383 0.9× 95 0.7× 81 0.9× 56 0.6× 28 1.3k
Dominique Desplancq France 15 806 1.5× 447 1.0× 121 0.9× 58 0.6× 43 0.5× 26 1.0k

Countries citing papers authored by Sara D’Angelo

Since Specialization
Citations

This map shows the geographic impact of Sara D’Angelo'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 Sara D’Angelo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sara D’Angelo more than expected).

Fields of papers citing papers by Sara D’Angelo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sara D’Angelo. 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 Sara D’Angelo. The network helps show where Sara D’Angelo may publish in the future.

Co-authorship network of co-authors of Sara D’Angelo

This figure shows the co-authorship network connecting the top 25 collaborators of Sara D’Angelo. A scholar is included among the top collaborators of Sara D’Angelo 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 Sara D’Angelo. Sara D’Angelo 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.
Bedinger, Daniel, Simon Cocklin, Sara D’Angelo, et al.. (2025). Identification of polyreactive antibodies by high throughput enzyme-linked immunosorbent assay and surface Plasmon resonance. Journal of Immunological Methods. 539. 113855–113855. 1 indexed citations
2.
Teixeira, André A., David Knight, Roberto Di Niro, et al.. (2025). Developing drug-like single-domain antibodies (VHH) from in vitro libraries. mAbs. 17(1). 2516676–2516676. 2 indexed citations
3.
Ferrara, Fortunato, André A. Teixeira, Sara D’Angelo, et al.. (2024). A next-generation Fab library platform directly yielding drug-like antibodies with high affinity, diversity, and developability. mAbs. 16(1). 2394230–2394230. 1 indexed citations
4.
Velappan, Nileena, Fortunato Ferrara, Sara D’Angelo, et al.. (2023). Direct selection of functional fluorescent-protein antibody fusions by yeast display. PLoS ONE. 18(2). e0280930–e0280930.
5.
6.
Ferrara, Fortunato, Sara D’Angelo, André A. Teixeira, et al.. (2022). Pandemic’s silver lining. mAbs. 14(1). 2133666–2133666. 1 indexed citations
7.
Teixeira, André A., Sara D’Angelo, Fortunato Ferrara, et al.. (2022). Simultaneous affinity maturation and developability enhancement using natural liability-free CDRs. mAbs. 14(1). 2115200–2115200. 10 indexed citations
8.
Ferrara, Fortunato, Sara D’Angelo, André A. Teixeira, et al.. (2022). A pandemic-enabled comparison of discovery platforms demonstrates a naive antibody library can match the best immune-sourced antibodies. Nature Communications. 13(1). 462–462. 24 indexed citations
9.
D’Angelo, Sara, Fortunato Ferrara, Leslie Naranjo, et al.. (2021). A single donor is sufficient to produce a highly functional in vitro antibody library. Communications Biology. 4(1). 350–350. 19 indexed citations
10.
Teixeira, André A., Sara D’Angelo, Leslie Naranjo, et al.. (2021). Drug-like antibodies with high affinity, diversity and developability directly from next-generation antibody libraries. mAbs. 13(1). 1980942–1980942. 39 indexed citations
11.
Naranjo, Leslie, Fortunato Ferrara, Nicolas Blanchard, et al.. (2019). Recombinant Antibodies against Mycolactone. Toxins. 11(6). 346–346. 6 indexed citations
12.
D’Angelo, Sara, Fernanda I. Staquicini, Fortunato Ferrara, et al.. (2018). Selection of phage-displayed accessible recombinant targeted antibodies (SPARTA): methodology and applications. JCI Insight. 3(9). 17 indexed citations
13.
Ferrara, Fortunato, Andrew Bradbury, & Sara D’Angelo. (2018). Primer Design and Inverse PCR on Yeast Display Antibody Selection Outputs. Methods in molecular biology. 1721. 35–45. 4 indexed citations
14.
Yao, Virginia J., Sara D’Angelo, Kimberly S. Butler, et al.. (2016). Ligand-targeted theranostic nanomedicines against cancer. Journal of Controlled Release. 240. 267–286. 152 indexed citations
15.
Glanville, Jacob, Sara D’Angelo, Tarik A. Khan, et al.. (2015). Deep sequencing in library selection projects: what insight does it bring?. Current Opinion in Structural Biology. 33. 146–160. 60 indexed citations
16.
Close, Devin, Sara D’Angelo, & Andrew Bradbury. (2014). A new family of β-helix proteins with similarities to the polysaccharide lyases. Acta Crystallographica Section D Biological Crystallography. 70(10). 2583–2592. 9 indexed citations
17.
D’Angelo, Sara, Flavio Mignone, Roberto Di Niro, et al.. (2013). Profiling celiac disease antibody repertoire. Clinical Immunology. 148(1). 99–109. 22 indexed citations
18.
D’Angelo, Sara, Nileena Velappan, Flavio Mignone, et al.. (2011). Filtering "genic" open reading frames from genomic DNA samples for advanced annotation. BMC Genomics. 12(S1). S5–S5. 20 indexed citations
19.
Niro, Roberto Di, Flavio Mignone, Sara D’Angelo, et al.. (2010). Rapid interactome profiling by massive sequencing. Nucleic Acids Research. 38(9). e110–e110. 91 indexed citations
20.
Niro, Roberto Di, et al.. (2009). Profiling the Autoantibody Repertoire by Screening Phage-Displayed Human cDNA Libraries. Methods in molecular biology. 570. 353–369. 10 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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