Raphaël Mourad

1.3k total citations · 1 hit paper
29 papers, 669 citations indexed

About

Raphaël Mourad is a scholar working on Molecular Biology, Pathology and Forensic Medicine and Genetics. According to data from OpenAlex, Raphaël Mourad has authored 29 papers receiving a total of 669 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 4 papers in Pathology and Forensic Medicine and 4 papers in Genetics. Recurrent topics in Raphaël Mourad's work include Genomics and Chromatin Dynamics (14 papers), RNA Research and Splicing (6 papers) and Bioinformatics and Genomic Networks (5 papers). Raphaël Mourad is often cited by papers focused on Genomics and Chromatin Dynamics (14 papers), RNA Research and Splicing (6 papers) and Bioinformatics and Genomic Networks (5 papers). Raphaël Mourad collaborates with scholars based in France, United States and Ukraine. Raphaël Mourad's co-authors include Vincent Rocher, Olivier Cuvier, Gaëlle Legube, Christine Sinoquet, Philippe Leray, Thomas Clouaire, Coline Arnould, Daan Noordermeer, Felix Zhou and James E. Haber and has published in prestigious journals such as Nature, Nucleic Acids Research and Nature Communications.

In The Last Decade

Raphaël Mourad

28 papers receiving 662 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.

Loop extrusion as a mechanism for formation of DNA damage repair foci

2021 207 citations Coline Arnould, Vincent Rocher et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Raphaël Mourad France	13	507	92	69	45	39	29	669
Yingbo Cui China	8	422 0.8×	45 0.5×	54 0.8×	25 0.6×	16 0.4×	26	511
Yong Lu United States	17	531 1.0×	72 0.8×	86 1.2×	23 0.5×	24 0.6×	35	835
Hongyu Zhao China	13	499 1.0×	103 1.1×	78 1.1×	18 0.4×	10 0.3×	42	764
Shijia Zhu United States	11	791 1.6×	83 0.9×	105 1.5×	12 0.3×	26 0.7×	28	987
Dan Tenenbaum United States	5	592 1.2×	44 0.5×	66 1.0×	27 0.6×	29 0.7×	8	788
Yen-Wei Chu Taiwan	10	387 0.8×	58 0.6×	68 1.0×	7 0.2×	19 0.5×	41	525
Stan Letovsky United States	7	786 1.6×	168 1.8×	88 1.3×	10 0.2×	22 0.6×	11	872
Seungwoo Hwang South Korea	14	282 0.6×	45 0.5×	62 0.9×	14 0.3×	23 0.6×	19	550
Qunhua Li United States	14	791 1.6×	184 2.0×	129 1.9×	26 0.6×	9 0.2×	35	997
Reza Mirzazadeh Sweden	11	658 1.3×	55 0.6×	100 1.4×	16 0.4×	29 0.7×	15	755

Countries citing papers authored by Raphaël Mourad

Since Specialization

Citations

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

Fields of papers citing papers by Raphaël Mourad

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Raphaël Mourad

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

All Works

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20 of 20 papers shown

Esnault, Cyril, Amal Zine El Aabidine, Marie-Cécile Robert, et al.. (2025). G-quadruplexes are promoter elements controlling nucleosome exclusion and RNA polymerase II pausing. Nature Genetics. 57(8). 1981–1993. 1 indexed citations

Brouard, Céline, Raphaël Mourad, & Nathalie Vialaneix. (2024). Should we really use graph neural networks for transcriptomic prediction?. Briefings in Bioinformatics. 25(2). 2 indexed citations

García, Eugene E., et al.. (2023). An Artificial Intelligence-Based Support Tool for Lumbar Spinal Stenosis Diagnosis from Self-Reported History Questionnaire. World Neurosurgery. 181. e953–e962. 3 indexed citations

Lebl, Darren R., et al.. (2023). A neural network model for detection and classification of lumbar spinal stenosis on MRI. European Spine Journal. 33(3). 941–948. 12 indexed citations

Arnould, Coline, Vincent Rocher, Aldo S. Bader, et al.. (2023). Chromatin compartmentalization regulates the response to DNA damage. Nature. 623(7985). 183–192. 88 indexed citations

Mourad, Raphaël. (2023). Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences. BMC Bioinformatics. 24(1). 186–186. 7 indexed citations

Cohen, Sarah, Aude Guénolé, Ikrame Lazar, et al.. (2022). A POLD3/BLM dependent pathway handles DSBs in transcribed chromatin upon excessive RNA:DNA hybrid accumulation. Nature Communications. 13(1). 2012–2012. 23 indexed citations

Mourad, Raphaël, et al.. (2022). Performance of hybrid artificial intelligence in determining candidacy for lumbar stenosis surgery. European Spine Journal. 31(8). 2149–2155. 11 indexed citations

Mourad, Raphaël. (2022). TADreg: a versatile regression framework for TAD identification, differential analysis and rearranged 3D genome prediction. BMC Bioinformatics. 23(1). 82–82. 5 indexed citations

10.

Rocher, Vincent, et al.. (2021). DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions. PLoS Computational Biology. 17(8). e1009308–e1009308. 26 indexed citations

11.

Arnould, Coline, Vincent Rocher, Anne-Laure Finoux, et al.. (2021). Loop extrusion as a mechanism for formation of DNA damage repair foci. Nature. 590(7847). 660–665. 207 indexed citations breakdown →

12.

Mourad, Raphaël. (2019). Studying 3D genome evolution using genomic sequence. Bioinformatics. 36(5). 1367–1373. 5 indexed citations

13.

Mourad, Raphaël, Krzysztof Ginalski, Gaëlle Legube, & Olivier Cuvier. (2018). Predicting double-strand DNA breaks using epigenome marks or DNA at kilobase resolution. Genome biology. 19(1). 34–34. 24 indexed citations

14.

Umlauf, David & Raphaël Mourad. (2018). The 3D genome: From fundamental principles to disease and cancer. Seminars in Cell and Developmental Biology. 90. 128–137. 13 indexed citations

15.

Mourad, Raphaël, Lang Li, & Olivier Cuvier. (2017). Uncovering direct and indirect molecular determinants of chromatin loops using a computational integrative approach. PLoS Computational Biology. 13(5). e1005538–e1005538. 6 indexed citations

16.

Mourad, Raphaël & Olivier Cuvier. (2016). Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation. PLoS Computational Biology. 12(5). e1004908–e1004908. 26 indexed citations

17.

Zhang, Pengyue, Raphaël Mourad, Yang Xiang, et al.. (2012). A dynamic time order network for time-series gene expression data analysis. BMC Systems Biology. 6(S3). S9–S9. 8 indexed citations

18.

Perduca, Vittorio, Christine Sinoquet, Raphaël Mourad, & Grégory Nuel. (2012). Alternative Methods for H1 Simulations in Genome-Wide Association Studies. Human Heredity. 73(2). 95–104. 5 indexed citations

19.

Mourad, Raphaël, Christine Sinoquet, & Philippe Leray. (2011). Probabilistic graphical models for genetic association studies. Briefings in Bioinformatics. 13(1). 20–33. 16 indexed citations

20.

Mourad, Raphaël, Christine Sinoquet, & Philippe Leray. (2011). A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies. BMC Bioinformatics. 12(1). 16–16. 39 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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