Flora Jay

7.7k total citations
23 papers, 1.1k citations indexed

About

Flora Jay is a scholar working on Genetics, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Flora Jay has authored 23 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Genetics, 7 papers in Molecular Biology and 3 papers in Artificial Intelligence. Recurrent topics in Flora Jay's work include Genetic diversity and population structure (12 papers), Forensic and Genetic Research (9 papers) and Genetic and phenotypic traits in livestock (6 papers). Flora Jay is often cited by papers focused on Genetic diversity and population structure (12 papers), Forensic and Genetic Research (9 papers) and Genetic and phenotypic traits in livestock (6 papers). Flora Jay collaborates with scholars based in France, United States and Spain. Flora Jay's co-authors include Olivier François, Michaël G. B. Blum, Éric Durand, Montgomery Slatkin, Per Sjödin, Mattias Jakobsson, Frédéric Austerlitz, Simon Boitard, Pontus Skoglund and Michael de Jongh and has published in prestigious journals such as Science, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Flora Jay

23 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Flora Jay France 13 730 223 166 160 156 23 1.1k
Nelson J. R. Fagundes Brazil 17 800 1.1× 358 1.6× 197 1.2× 162 1.0× 139 0.9× 59 1.5k
Jonathan Terhorst United States 11 749 1.0× 325 1.5× 125 0.8× 65 0.4× 77 0.5× 26 1.1k
Jack Kamm United States 10 630 0.9× 269 1.2× 116 0.7× 72 0.5× 82 0.5× 15 951
Stefano Mona France 22 888 1.2× 298 1.3× 335 2.0× 68 0.4× 43 0.3× 43 1.3k
Stephan Schiffels Germany 13 1.3k 1.7× 461 2.1× 208 1.3× 327 2.0× 180 1.2× 25 1.7k
Andrea Benazzo Italy 19 562 0.8× 227 1.0× 205 1.2× 70 0.4× 32 0.2× 35 965
Michael DeGiorgio United States 20 865 1.2× 470 2.1× 141 0.8× 71 0.4× 45 0.3× 57 1.2k
Kelley Harris United States 17 774 1.1× 698 3.1× 92 0.6× 85 0.5× 109 0.7× 33 1.4k
Franz Manni France 16 994 1.4× 315 1.4× 314 1.9× 87 0.5× 42 0.3× 34 1.6k
José Alfredo Samaniego Castruita Denmark 18 623 0.9× 527 2.4× 429 2.6× 146 0.9× 66 0.4× 33 1.4k

Countries citing papers authored by Flora Jay

Since Specialization
Citations

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

Fields of papers citing papers by Flora Jay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Flora Jay

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

All Works

20 of 20 papers shown
1.
Villanea, Fernando A., David Peede, Kelsey E. Witt, et al.. (2025). The MUC19 gene: An evolutionary history of recurrent introgression and natural selection. Science. 389(6762). eadl0882–eadl0882. 1 indexed citations
2.
Austerlitz, Frédéric, et al.. (2025). Assessing simulation-based supervised machine learning for demographic parameter inference from genomic data. Heredity. 134(7). 417–426. 1 indexed citations
3.
Martin, Michael D., et al.. (2024). GRUPS-rs, a high-performance ancient DNA genetic relatednessestimation software relying on pedigree simulations. SPIRE - Sciences Po Institutional REpository. 1–34. 1 indexed citations
4.
Achaz, Guillaume, Jean Cury, Bruno Toupance, et al.. (2023). Cultural transmission of reproductive success impacts genomic diversity, coalescent tree topologies, and demographic inferences. Genetics. 223(4). 4 indexed citations
5.
Decelle, Aurélien, et al.. (2023). Deep convolutional and conditional neural networks for large-scale genomic data generation. PLoS Computational Biology. 19(10). e1011584–e1011584. 6 indexed citations
6.
Bray, Erik M., et al.. (2022). dnadna: a deep learning framework for population genetics inference. Bioinformatics. 39(1). 4 indexed citations
7.
Cury, Jean, Benjamin C. Haller, Guillaume Achaz, & Flora Jay. (2022). Simulation of bacterial populations with SLiM. SHILAP Revista de lepidopterología. 2. 10 indexed citations
8.
Decelle, Aurélien, Linda Ongaro, Davide Marnetto, et al.. (2021). Creating artificial human genomes using generative neural networks. PLoS Genetics. 17(2). e1009303–e1009303. 68 indexed citations
9.
Cury, Jean, et al.. (2020). Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation. Molecular Ecology Resources. 21(8). 2645–2660. 44 indexed citations
10.
François, Olivier & Flora Jay. (2020). Factor analysis of ancient population genomic samples. Nature Communications. 11(1). 4661–4661. 22 indexed citations
11.
Caye, Kévin, Flora Jay, Olivier Michel, & Olivier François. (2018). Fast inference of individual admixture coefficients using geographic data. The Annals of Applied Statistics. 12(1). 50 indexed citations
12.
Boitard, Simon, Willy Rodríguez, Flora Jay, Stefano Mona, & Frédéric Austerlitz. (2016). Inferring Population Size History from Large Samples of Genome-Wide Molecular Data - An Approximate Bayesian Computation Approach. PLoS Genetics. 12(3). e1005877–e1005877. 104 indexed citations
13.
Jay, Flora, Olivier François, Éric Durand, & Michaël G. B. Blum. (2015). POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models. Journal of Statistical Software. 68(9). 15 indexed citations
14.
Pons-Salort, Margarita, Jordi Serra‐Cobo, Flora Jay, et al.. (2014). Insights into Persistence Mechanisms of a Zoonotic Virus in Bat Colonies Using a Multispecies Metapopulation Model. PLoS ONE. 9(4). e95610–e95610. 12 indexed citations
15.
Wall, Jeffrey D., Melinda A. Yang, Flora Jay, et al.. (2013). Higher Levels of Neanderthal Ancestry in East Asians than in Europeans. Genetics. 194(1). 199–209. 164 indexed citations
16.
Cahill, James A., Richard E. Green, Tara L. Fulton, et al.. (2013). Genomic Evidence for Island Population Conversion Resolves Conflicting Theories of Polar Bear Evolution. PLoS Genetics. 9(3). e1003345–e1003345. 117 indexed citations
17.
Schlebusch, Carina M., Pontus Skoglund, Per Sjödin, et al.. (2012). Genomic Variation in Seven Khoe-San Groups Reveals Adaptation and Complex African History. Science. 338(6105). 374–379. 258 indexed citations
18.
Jay, Flora, Stéphanie Manel, Nadir Álvarez, et al.. (2012). Forecasting changes in population genetic structure of alpine plants in response to global warming. Molecular Ecology. 21(10). 2354–2368. 113 indexed citations
19.
Jay, Flora, Per Sjödin, Mattias Jakobsson, & Michaël G. B. Blum. (2012). Anisotropic Isolation by Distance: The Main Orientations of Human Genetic Differentiation. Molecular Biology and Evolution. 30(3). 513–525. 26 indexed citations
20.
Jay, Flora, Olivier François, & Michaël G. B. Blum. (2011). Predictions of Native American Population Structure Using Linguistic Covariates in a Hidden Regression Framework. PLoS ONE. 6(1). e16227–e16227. 8 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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