Gabriel Casella

647 citations
9 papers · 343 · h-index 4

Impact in

  • Aging top 10%
    • Genetics, Aging, and Longevity in Model Organisms
    • Telomeres, Telomerase, and Senescence

Papers in

    • Single-cell and spatial transcriptomics 2
    • Gene expression and cancer classification 1
    • Cell Image Analysis Techniques 4

Gabriel Casella

7 papers receiving 342 citations

Peers

Gabriel Casella
Comparison fields: 5 of 63
  • Aging 30
  • Physiology 149
  • Cancer Research 64
  • Immunology 73
  • Molecular Biology 181
Replace José Mario González-Meljem with:
José Mario González-Meljem United Kingdom
Ayush Midha United States
Ben S. Lam United States
Qun Chu China
Seongki Min South Korea
Sakie Katsumura Japan
Vaja Chipashvili United States
Kathryn C. Jackson United States
Xian Jin Lian Canada
Kasper Derks Netherlands
Gabriel Casella relative to José Mario González-Meljem United Kingdom José Mario González-Meljem's profile →
Citations per field
00.5×1.5×
José Mario González-Meljem · 1×
Citations per year

Countries citing papers authored by Gabriel Casella

Since Specialization
Citations

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

Fields of papers citing papers by Gabriel Casella

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 19 scholars most cited alongside Gabriel Casella, 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 Gabriel Casella Line = papers co-authored together Gabriel Casella links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 2019250
2 201947
3 202233
4 201710
5 20231
6 20231
7 20241
8 20250
9 20240

About Gabriel Casella

Gabriel Casella is a scholar working on Molecular Biology, Biophysics, Rheumatology, Immunology and Cancer Research, having authored 9 papers that have together received 343 indexed citations. Recurring topics across this work include Cell Image Analysis Techniques (4 papers), Single-cell and spatial transcriptomics (2 papers), Atherosclerosis and Cardiovascular Diseases (2 papers), Systemic Lupus Erythematosus Research (2 papers), Medical Image Segmentation Techniques (1 paper), Telomeres, Telomerase, and Senescence (1 paper), Gene expression and cancer classification (1 paper) and T-cell and B-cell Immunology (1 paper). The work is most often cited by research in Aging (30 citations), Physiology (149 citations), Cancer Research (64 citations), Immunology (73 citations) and Molecular Biology (181 citations). Gabriel Casella has collaborated with scholars based in United States and Japan. Frequent co-authors include Kotb Abdelmohsen, Myriam Gorospe, Kyoung Mi Kim, Supriyo De, Yulan Piao, Rachel Munk, Dimitrios Tsitsipatis, Junting Ai, Marcus R. Clark and Maryellen L. Giger. Their work appears in journals such as Journal of Clinical Investigation, Nucleic Acids Research, Clinical Neurology and Neurosurgery, Wiley Interdisciplinary Reviews - RNA and Journal of Medical Imaging.

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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