David Simoncini

612 citations
19 papers · 337 · h-index 11

Impact in

    • Protein Structure and Dynamics
    • RNA and protein synthesis mechanisms
    • Machine Learning in Bioinformatics
    • Glycosylation and Glycoproteins Research
    • Genomics and Phylogenetic Studies
    • Enzyme Structure and Function

Papers in

    • Protein Structure and Dynamics 12
    • RNA and protein synthesis mechanisms 6
    • Genomics and Phylogenetic Studies 3
    • Glycosylation and Glycoproteins Research 3
    • Enzyme Structure and Function 11

David Simoncini

19 papers receiving 337 citations

Peers

David Simoncini
Comparison fields: 5 of 55
  • Molecular Biology 274
  • Materials Chemistry 135
  • Computational Theory and Mathematics 40
  • Health Informatics 2
  • Structural Biology 2
Replace Elizabeth M. Meiering with:
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David Simoncini relative to Elizabeth M. Meiering Canada Elizabeth M. Meiering's profile →
Citations per field
00.5×1.5×
Elizabeth M. Meiering · 1×
Citations per year

Countries citing papers authored by David Simoncini

Since Specialization
Citations

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

Fields of papers citing papers by David Simoncini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Simoncini. 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 David Simoncini. The network helps show where David Simoncini may publish in the future.

Co-authors

The 25 scholars most cited alongside David Simoncini, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with David Simoncini Line = papers co-authored together David Simoncini links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 201499
2 201234
3 201534
4 201827
5 201321
6 202120
7 201116
8 201913
9 201712
10 201512
11 202410
12 201610
13 201810
14 20116
15 20184
16 20124
17 20212
18 20122
19 20241

About David Simoncini

David Simoncini is a scholar working on Molecular Biology, Materials Chemistry, Computational Theory and Mathematics, Artificial Intelligence and Genetics, having authored 19 papers that have together received 337 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (12 papers), Enzyme Structure and Function (11 papers), RNA and protein synthesis mechanisms (6 papers), Genomics and Phylogenetic Studies (3 papers), Glycosylation and Glycoproteins Research (3 papers), Computational Drug Discovery Methods (2 papers), Metaheuristic Optimization Algorithms Research (2 papers) and Mass Spectrometry Techniques and Applications (2 papers). The work is most often cited by research in Molecular Biology (274 citations), Materials Chemistry (135 citations), Computational Theory and Mathematics (40 citations), Health Informatics (2 citations) and Structural Biology (2 citations). David Simoncini has collaborated with scholars based in France, Japan and Belgium. Frequent co-authors include Kam Y. J. Zhang, Thomas Schiex, Jeremy R. H. Tame, Arnout Voet, Christine Addy, Sophie Barbe, Hiroki Noguchi, Francois Berenger, Daiki Terada and Sam‐Yong Park. Their work appears in journals such as PLoS ONE, Bioinformatics, Journal of Chemical Theory and Computation, Journal of the American Chemical Society and IUCrJ.

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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