Aurélie Kamoun

2.9k citations
16 papers · 985 indexed · 1 hit paper · h-index 11
Topics
Glioma Diagnosis and Treatment (4 papers)Cancer Genomics and Diagnostics (4 papers)Radiomics and Machine Learning in Medical Imaging (3 papers)
Partner nations
FranceGuadeloupeSpain

In The Last Decade

Aurélie Kamoun

14 papers receiving 975 citations

Hit Papers

A deep learning model to predict RNA-Seq expression of tu...2020202620222024202050100150200250

Peers

Aurélie Kamoun
Comparison fields: 5 of 90
  • Molecular Biology 352
  • Cancer Research 284
  • Genetics 271
  • Radiology, Nuclear Medicine and Imaging 230
  • Pulmonary and Respiratory Medicine 202
Replace MacLean P. Nasrallah with:
MacLean P. Nasrallah United States
Matthew Schniederjan United States
Friedrich Feuerhake Germany
Zev A. Binder United States
Andra Krauze United States
Jorge Samanamud United States
Sabine Taschner‐Mandl Austria
Guillaume Bataillon France
Hiro Shimada United States
Clinton J.V. Campbell Canada
Aurélie Kamoun relative to MacLean P. Nasrallah United States MacLean P. Nasrallah's profile →
Citations per field
00.5×3.6×
MacLean P. Nasrallah · 1×
Citations per year

Countries citing papers authored by Aurélie Kamoun

Since Specialization
Citations

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

Fields of papers citing papers by Aurélie Kamoun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aurélie Kamoun

This figure shows the co-authorship network connecting the top 25 collaborators of Aurélie Kamoun. A scholar is included among the top collaborators of Aurélie Kamoun based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aurélie Kamoun. Aurélie Kamoun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
#WorkIndexed citations
1 0
2 2
3 51
4 3
5 0
6
A deep learning model to predict RNA-Seq expression of tumours from whole slide imagesbreakdown →
282
7 52
8 86
9 176
10 33
11 28
12 6
13 117
14 27
15 38
16 84

About Aurélie Kamoun

Aurélie Kamoun is a scholar working on Health Informatics, Cancer Research and Family Practice, having authored 16 papers that have together received 985 indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (4 papers), Cancer Genomics and Diagnostics (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). The work is most often cited by research in Genetics (271 citations), Cancer Research (284 citations) and Health Informatics (16 citations). Aurélie Kamoun has collaborated with scholars based in France, Guadeloupe and Spain. Frequent co-authors include Charlie Saillard, Meriem Sefta, Thomas Clozel, Benoît Schmauch, Pierre Courtiol, Alberto Romagnoni, Matahi Moarii, Gilles Wainrib, Julien Caldéraro and Pascale Maillé. Their work appears in journals such as Nature Communications, Journal of Clinical Oncology 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.

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