Gabriele Partel

408 citations
9 papers · 205 · h-index 4

Impact in

  • Biophysics top 5%
    • Cell Image Analysis Techniques
    • Advanced Fluorescence Microscopy Techniques
    • Image Processing Techniques and Applications

Papers in

    • Single-cell and spatial transcriptomics 6
    • Gene expression and cancer classification 3
    • Advanced biosensing and bioanalysis techniques 1
    • Cell Image Analysis Techniques 3
    • Advanced Fluorescence Microscopy Techniques 1

Gabriele Partel

9 papers receiving 204 citations

Peers

Gabriele Partel
Comparison fields: 5 of 60
  • Biophysics 82
  • Media Technology 28
  • Health Informatics 3
  • Structural Biology 3
  • Molecular Biology 100
Replace Håkan Wieslander with:
Håkan Wieslander Sweden
Ervin Tasnádi Hungary
Marcin Kociołek Poland
Shenghua He United States
Tim Scherr Germany
Gus Ferguson United Kingdom
Dmitry V. Sorokin Russia
Lucas von Chamier United Kingdom
Kristian Kvilekval United States
Armaghan W. Naik United States
Gabriele Partel relative to Håkan Wieslander Sweden Håkan Wieslander's profile →
Citations per field
00.5×1.6×
Håkan Wieslander · 1×
Citations per year

Countries citing papers authored by Gabriele Partel

Since Specialization
Citations

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

Fields of papers citing papers by Gabriele Partel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Gabriele Partel, 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 Gabriele Partel Line = papers co-authored together Gabriele Partel links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 2018130
2 202027
3 202020
4 202019
5 20223
6 20242
7 20212
8 20211
9 20201

About Gabriele Partel

Gabriele Partel is a scholar working on Molecular Biology, Biophysics, Artificial Intelligence, Media Technology and Spectroscopy, having authored 9 papers that have together received 205 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (6 papers), Gene expression and cancer classification (3 papers), Cell Image Analysis Techniques (3 papers), Advanced Proteomics Techniques and Applications (1 paper), Advanced Fluorescence Microscopy Techniques (1 paper), Advanced biosensing and bioanalysis techniques (1 paper), Image Processing Techniques and Applications (1 paper) and AI in cancer detection (1 paper). The work is most often cited by research in Biophysics (82 citations), Media Technology (28 citations), Health Informatics (3 citations), Structural Biology (3 citations) and Molecular Biology (100 citations). Gabriele Partel has collaborated with scholars based in Sweden, Finland and Belgium. Frequent co-authors include Carolina Wählby, Leslie Solorzano, Anna H. Klemm, Amit Suveer, Kimmo Kartasalo, Ola Spjuth, Philip J. Harrison, Anindya Gupta, Ida‐Maria Sintorn and Håkan Wieslander. Their work appears in journals such as Bioinformatics, FEBS Journal, BMC Biology, PLoS Computational Biology and Cytometry Part A.

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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