Meriem Sefta

1.5k total citations · 2 hit papers
6 papers, 688 citations indexed

About

Meriem Sefta is a scholar working on Oncology, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Meriem Sefta has authored 6 papers receiving a total of 688 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Oncology, 2 papers in Molecular Biology and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Meriem Sefta's work include AI in cancer detection (2 papers), Cancer Genomics and Diagnostics (2 papers) and Pancreatic and Hepatic Oncology Research (1 paper). Meriem Sefta is often cited by papers focused on AI in cancer detection (2 papers), Cancer Genomics and Diagnostics (2 papers) and Pancreatic and Hepatic Oncology Research (1 paper). Meriem Sefta collaborates with scholars based in France, United States and Canada. Meriem Sefta's co-authors include Thomas Clozel, Mikhail Zaslavskiy, Elodie Pronier, Gilles Wainrib, Pierre Courtiol, Matahi Moarii, Charlie Saillard, Aurélie Kamoun, Benoît Schmauch and Julien Caldéraro and has published in prestigious journals such as Nature Medicine, Nature Communications and Cancer Research.

In The Last Decade

Meriem Sefta

6 papers receiving 685 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning-based classification of mesothelioma improves prediction of patient outcome

2019 314 citations Pierre Courtiol, Charles Maussion et al. Nature Medicine profile →
A deep learning model to predict RNA-Seq expression of tumours from whole slide images

2020 282 citations Benoît Schmauch, Alberto Romagnoni et al. Nature Communications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Meriem Sefta France	5	347	305	157	155	143	6	688
Pierre Courtiol France	4	436 1.3×	418 1.4×	164 1.0×	182 1.2×	135 0.9×	7	815
Charlie Saillard France	6	379 1.1×	433 1.4×	146 0.9×	145 0.9×	116 0.8×	12	754
Benoît Schmauch France	8	370 1.1×	448 1.5×	126 0.8×	134 0.9×	111 0.8×	14	763
Amelie Echle Germany	9	362 1.0×	350 1.1×	198 1.3×	115 0.7×	84 0.6×	10	618
Richard Colling United Kingdom	18	318 0.9×	263 0.9×	252 1.6×	122 0.8×	124 0.9×	47	769
Mane Williams United States	5	431 1.2×	338 1.1×	107 0.7×	114 0.7×	127 0.9×	6	707
Matahi Moarii France	9	434 1.3×	422 1.4×	215 1.4×	251 1.6×	291 2.0×	12	1.1k
Jeremias Krause Germany	3	466 1.3×	489 1.6×	305 1.9×	198 1.3×	109 0.8×	8	868
Xiangxue Wang United States	13	339 1.0×	444 1.5×	241 1.5×	117 0.8×	99 0.7×	31	759
Elodie Pronier United States	13	434 1.3×	419 1.4×	194 1.2×	216 1.4×	424 3.0×	17	1.2k

Countries citing papers authored by Meriem Sefta

Since Specialization

Citations

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

Fields of papers citing papers by Meriem Sefta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meriem Sefta

This figure shows the co-authorship network connecting the top 25 collaborators of Meriem Sefta. A scholar is included among the top collaborators of Meriem Sefta 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 Meriem Sefta. Meriem Sefta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Saillard, Charlie, Rémy Dubois, Nicolas Loiseau, et al.. (2023). Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides. Nature Communications. 14(1). 6695–6695. 51 indexed citations

2.

Schmauch, Benoît, Alberto Romagnoni, Elodie Pronier, et al.. (2020). A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nature Communications. 11(1). 3877–3877. 282 indexed citations breakdown →

3.

Pronier, Elodie, Benoît Schmauch, Alberto Romagnoni, et al.. (2020). Abstract 2105: HE2RNA: A deep learning model for transcriptomic learning from digital pathology. Cancer Research. 80(16_Supplement). 2105–2105. 2 indexed citations

4.

Courtiol, Pierre, Charles Maussion, Matahi Moarii, et al.. (2019). Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nature Medicine. 25(10). 1519–1525. 314 indexed citations breakdown →

5.

Eloy, Philippine, Catherine Dehainault, Meriem Sefta, et al.. (2016). A Parent-of-Origin Effect Impacts the Phenotype in Low Penetrance Retinoblastoma Families Segregating the c.1981C>T/p.Arg661Trp Mutation of RB1. PLoS Genetics. 12(2). e1005888–e1005888. 26 indexed citations

6.

Gallud, Audrey, David Warther, Marie Maynadier, et al.. (2015). Identification of MRC2 and CD209 receptors as targets for photodynamic therapy of retinoblastoma using mesoporous silica nanoparticles. RSC Advances. 5(92). 75167–75172. 13 indexed citations

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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