Julia Marchetti
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- RNA and protein synthesis mechanisms
- Machine Learning in Bioinformatics
- Microbial Metabolic Engineering and Bioproduction
- Bioinformatics and Genomic Networks
- Genomics and Phylogenetic Studies
Papers in
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- Protein Structure and Dynamics 3
- RNA and protein synthesis mechanisms 2
- Genomics and Phylogenetic Studies 2
- Genetics, Bioinformatics, and Biomedical Research 1
- Machine Learning in Bioinformatics 1
- RNA Research and Splicing 1
- Co-authors
- Marı́a Silvina Fornasari (4 shared papers)Gustavo Parisi (4 shared papers)Nicolás Palópoli (4 shared papers)Ana Julia Velez Rueda (2 shared papers)Diego Javier Zea (2 shared papers)Nahuel Escobedo (1 shared paper)Tadeo E. Saldaño (2 shared papers)Sebastian Fernández-Alberti (1 shared paper)
In The Last Decade
Julia Marchetti
5 papers receiving 114 citations
Peers
Comparison fields: 5 of 31
- Computational Theory and Mathematics 30
- Molecular Biology 99
- Materials Chemistry 39
- Biophysics 2
- Spectroscopy 5
Countries citing papers authored by Julia Marchetti
This map shows the geographic impact of Julia Marchetti'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 Julia Marchetti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julia Marchetti more than expected).
Fields of papers citing papers by Julia Marchetti
This network shows the impact of papers produced by Julia Marchetti. 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 Julia Marchetti. The network helps show where Julia Marchetti may publish in the future.
Co-authors
The 12 scholars most cited alongside Julia Marchetti, 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 | 2022 | 99 | |
| 2 | 2019 | 7 | |
| 3 | 2019 | 4 | |
| 4 | 2020 | 3 | |
| 5 | 2023 | 1 |
About Julia Marchetti
Julia Marchetti is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Computational Theory and Mathematics, Materials Chemistry and Infectious Diseases, having authored 5 papers that have together received 114 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (3 papers), RNA and protein synthesis mechanisms (2 papers), Genomics and Phylogenetic Studies (2 papers), Genetics, Bioinformatics, and Biomedical Research (1 paper), Machine Learning in Bioinformatics (1 paper), RNA Research and Splicing (1 paper), Computational Drug Discovery Methods (1 paper) and Enzyme Structure and Function (1 paper). The work is most often cited by research in Computational Theory and Mathematics (30 citations), Molecular Biology (99 citations), Materials Chemistry (39 citations), Biophysics (2 citations) and Spectroscopy (5 citations). Julia Marchetti has collaborated with scholars based in Argentina, Italy and France. Frequent co-authors include Marı́a Silvina Fornasari, Gustavo Parisi, Nicolás Palópoli, Ana Julia Velez Rueda, Diego Javier Zea, Nahuel Escobedo, Tadeo E. Saldaño, Sebastian Fernández-Alberti, Silvio C. E. Tosatto and Alexander Miguel Monzón. Their work appears in journals such as Journal of Molecular Biology, PLoS Computational Biology, PLoS ONE and Bioinformatics.
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.