Isabella Ellinger

66 papers receiving 2.1k citations

Peers

Isabella Ellinger
Comparison fields: 5 of 139
  • Obstetrics and Gynecology 227
  • Oncology 577
  • Biophysics 93
  • Radiology, Nuclear Medicine and Imaging 321
  • Artificial Intelligence 469
Replace Ali M. Ardekani with:
Ali M. Ardekani Iran
Vivek Narayan United States
Rami Mahfouz Lebanon
Zongfang Li China
Chiang‐Ching Huang United States
Xiaolin Zhu China
Johannes Lotz Germany
Lana X. Garmire United States
Cecilia Lindskog Sweden
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Citations per year

Countries citing papers authored by Isabella Ellinger

Since Specialization
Citations

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

Fields of papers citing papers by Isabella Ellinger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 69 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015240
2 2020227
3 2018198
4 2018193
5 1999104
6 202090
7 200487
8 202181
9 199971
10 201169
11 200151
12 201551
13 202239
14 201638
15 200138
16 200934
17 201830
18 200529
19 202427
20 201526

About Isabella Ellinger

Isabella Ellinger is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging, Immunology, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 69 papers that have together received 2.2k indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (11 papers), Monoclonal and Polyclonal Antibodies Research (11 papers), AI in cancer detection (11 papers), Cell Image Analysis Techniques (8 papers), Pregnancy and preeclampsia studies (7 papers), Glycosylation and Glycoproteins Research (6 papers), Reproductive System and Pregnancy (6 papers) and Allergic Rhinitis and Sensitization (5 papers). The work is most often cited by research in Obstetrics and Gynecology (227 citations), Oncology (577 citations), Biophysics (93 citations), Radiology, Nuclear Medicine and Imaging (321 citations) and Artificial Intelligence (469 citations). Isabella Ellinger has collaborated with scholars based in Austria, United Kingdom and Germany. Frequent co-authors include Amirreza Mahbod, Rupert Ecker, Renate Fuchs, Gerald Schaefer, Chunliang Wang, Waranya Chatuphonprasert, Kanokwan Jarukamjorn, Georg Dorffner, Anastasia Meshcheryakova and Ursula Föger‐Samwald. Their work appears in journals such as Placenta, International Journal of Molecular Sciences, European Journal of Immunology, Electrophoresis and Archives of Toxicology.

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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