Giulia Muzio
Impact in
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- Computational Drug Discovery Methods
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- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Machine Learning in Bioinformatics
- Gene Regulatory Network Analysis
- Single-cell and spatial transcriptomics
Papers in
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- Bioinformatics and Genomic Networks 3
- Advanced biosensing and bioanalysis techniques 1
- Machine Learning in Bioinformatics 1
- Genetics 2
- Genetic Associations and Epidemiology 2
- Co-authors
- Karsten Borgwardt (3 shared papers)Leslie O’Bray (2 shared papers)Hamideh Ramezani (1 shared paper)Murat Kuşcu (1 shared paper)Laetitia Meng-Papaxanthos (1 shared paper)Krista Fischer (1 shared paper)Tooba Khan (1 shared paper)Özgür B. Akan (1 shared paper)
- Journals
- Bioinformatics (2 papers)Briefings in Bioinformatics (1 paper)Apollo (University of Cambridge) (1 paper)
- Partner nations
- SwitzerlandItalyGermany
In The Last Decade
Giulia Muzio
5 papers receiving 153 citations
Peers
Comparison fields: 5 of 57
- Computational Theory and Mathematics 37
- Molecular Biology 109
- Biophysics 7
- Artificial Intelligence 30
- Statistical and Nonlinear Physics 10
Countries citing papers authored by Giulia Muzio
This map shows the geographic impact of Giulia Muzio'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 Giulia Muzio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giulia Muzio more than expected).
Fields of papers citing papers by Giulia Muzio
This network shows the impact of papers produced by Giulia Muzio. 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 Giulia Muzio. The network helps show where Giulia Muzio may publish in the future.
Co-authors
The 8 scholars most cited alongside Giulia Muzio, 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 | 2020 | 142 | |
| 2 | 2023 | 5 | |
| 3 | 2018 | 4 | |
| 4 | 2023 | 2 | |
| 5 | 2018 | 2 |
About Giulia Muzio
Giulia Muzio is a scholar working on Molecular Biology, Genetics, Biomedical Engineering, Cellular and Molecular Neuroscience and Computational Theory and Mathematics, having authored 5 papers that have together received 155 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (3 papers), Genetic Associations and Epidemiology (2 papers), Molecular Communication and Nanonetworks (2 papers), Advanced biosensing and bioanalysis techniques (1 paper), Machine Learning in Bioinformatics (1 paper), Computational Drug Discovery Methods (1 paper), Neuroscience and Neural Engineering (1 paper) and Wireless Body Area Networks (1 paper). The work is most often cited by research in Computational Theory and Mathematics (37 citations), Molecular Biology (109 citations), Biophysics (7 citations), Artificial Intelligence (30 citations) and Statistical and Nonlinear Physics (10 citations). Giulia Muzio has collaborated with scholars based in Switzerland, Italy and Germany. Frequent co-authors include Karsten Borgwardt, Leslie O’Bray, Hamideh Ramezani, Murat Kuşcu, Laetitia Meng-Papaxanthos, Krista Fischer, Tooba Khan and Özgür B. Akan. Their work appears in journals such as Bioinformatics, Briefings in Bioinformatics and Apollo (University of Cambridge).
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.