Marie Zufferey

627 total citations
8 papers, 330 citations indexed

About

Marie Zufferey is a scholar working on Molecular Biology, Computational Theory and Mathematics and Biophysics. According to data from OpenAlex, Marie Zufferey has authored 8 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 1 paper in Computational Theory and Mathematics and 1 paper in Biophysics. Recurrent topics in Marie Zufferey's work include Genomics and Chromatin Dynamics (5 papers), Bioinformatics and Genomic Networks (4 papers) and Gene expression and cancer classification (3 papers). Marie Zufferey is often cited by papers focused on Genomics and Chromatin Dynamics (5 papers), Bioinformatics and Genomic Networks (4 papers) and Gene expression and cancer classification (3 papers). Marie Zufferey collaborates with scholars based in Switzerland, United Kingdom and Italy. Marie Zufferey's co-authors include Daniele Tavernari, Giovanni Ciriello, Elisa Oricchio, Marco Mina, Stéphanie Sungalee, Dónal Landers, Yuanlong Liu, Kyle M. Douglass, Maria Donaldson Collier and Suliana Manley and has published in prestigious journals such as Nature Communications, Nature Genetics and Genome biology.

In The Last Decade

Marie Zufferey

8 papers receiving 325 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Marie Zufferey Switzerland	5	293	66	41	38	16	8	330
Andrian Yang Australia	9	224 0.8×	40 0.6×	26 0.6×	20 0.5×	6 0.4×	14	281
Jacob M. Tome United States	6	416 1.4×	33 0.5×	52 1.3×	44 1.2×	7 0.4×	7	449
Matthew Gosden United Kingdom	7	394 1.3×	101 1.5×	72 1.8×	30 0.8×	8 0.5×	9	451
Pol Cuscó Spain	5	202 0.7×	23 0.3×	26 0.6×	27 0.7×	9 0.6×	6	241
Mehran Karimzadeh Canada	8	253 0.9×	37 0.6×	39 1.0×	79 2.1×	20 1.3×	17	310
Simon Zhongyuan Tian China	9	330 1.1×	92 1.4×	43 1.0×	29 0.8×	15 0.9×	14	372
Marta Bleda Spain	9	189 0.6×	17 0.3×	79 1.9×	41 1.1×	17 1.1×	14	280
Antoine Baker France	12	429 1.5×	51 0.8×	76 1.9×	34 0.9×	6 0.4×	17	472
Ronen Sadeh Israel	9	421 1.4×	41 0.6×	50 1.2×	23 0.6×	23 1.4×	9	465
Galip Gürkan Yardımcı United States	6	466 1.6×	157 2.4×	91 2.2×	43 1.1×	10 0.6×	6	502

Countries citing papers authored by Marie Zufferey

Since Specialization

Citations

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

Fields of papers citing papers by Marie Zufferey

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marie Zufferey

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

All Works

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8 of 8 papers shown

Wysocka, Magdalena, Oskar Wysocki, Marie Zufferey, Dónal Landers, & André Freitas. (2023). A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data. BMC Bioinformatics. 24(1). 198–198. 32 indexed citations

Wysocki, Oskar, et al.. (2022). A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data. Research Square. 4 indexed citations

Liu, Yuanlong, Luca Nanni, Stéphanie Sungalee, et al.. (2021). Systematic inference and comparison of multi-scale chromatin sub-compartments connects spatial organization to cell phenotypes. Nature Communications. 12(1). 2439–2439. 52 indexed citations

Zufferey, Marie, Yuanlong Liu, Daniele Tavernari, Marco Mina, & Giovanni Ciriello. (2021). Systematic assessment of gene co-regulation within chromatin domains determines differentially active domains across human cancers. Genome biology. 22(1). 218–218. 4 indexed citations

Zufferey, Marie, Daniele Tavernari, & Giovanni Ciriello. (2021). Methods for the Analysis of Topologically Associating Domains (TADs). Methods in molecular biology. 2301. 39–59. 3 indexed citations

Collier, Maria Donaldson, Stéphanie Sungalee, Marie Zufferey, et al.. (2019). EZH2 oncogenic mutations drive epigenetic, transcriptional, and structural changes within chromatin domains. Nature Genetics. 51(3). 517–528. 92 indexed citations

Zufferey, Marie, Daniele Tavernari, Elisa Oricchio, & Giovanni Ciriello. (2018). Comparison of computational methods for the identification of topologically associating domains. Genome biology. 19(1). 217–217. 141 indexed citations

Zufferey, Marie & Daniele Tavernari. (2018). CSOgroup/TAD-benchmarking-scripts: TAD-benchmarking-scripts. Zenodo (CERN European Organization for Nuclear Research). 2 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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