David Causeur

972 total citations
40 papers, 630 citations indexed

About

David Causeur is a scholar working on Molecular Biology, Statistics and Probability and Management Science and Operations Research. According to data from OpenAlex, David Causeur has authored 40 papers receiving a total of 630 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 16 papers in Statistics and Probability and 8 papers in Management Science and Operations Research. Recurrent topics in David Causeur's work include Gene expression and cancer classification (10 papers), Statistical Methods and Bayesian Inference (7 papers) and Statistical Methods in Clinical Trials (7 papers). David Causeur is often cited by papers focused on Gene expression and cancer classification (10 papers), Statistical Methods and Bayesian Inference (7 papers) and Statistical Methods in Clinical Trials (7 papers). David Causeur collaborates with scholars based in France, Morocco and Taiwan. David Causeur's co-authors include Chloé Friguet, Maëla Kloareg, Sandrine Lagarrigue, Anne‐Laure Boulesteix, Roman Hornung, Florence Gondret, Annie Vincent, Anne Siegel, Yuna Blum and Julien Jardin and has published in prestigious journals such as Journal of the American Statistical Association, Bioinformatics and Food Chemistry.

In The Last Decade

David Causeur

37 papers receiving 614 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Causeur France 16 221 181 134 99 69 40 630
Yong-Min Cho South Korea 15 236 1.1× 348 1.9× 166 1.2× 7 0.1× 63 0.9× 40 734
Mitsunori Kayano Japan 13 129 0.6× 70 0.4× 27 0.2× 16 0.2× 116 1.7× 52 423
J. Altarriba Spain 20 109 0.5× 852 4.7× 328 2.4× 15 0.2× 283 4.1× 54 1.1k
Tristan Mary‐Huard France 19 294 1.3× 533 2.9× 60 0.4× 18 0.2× 148 2.1× 60 1.1k
A. Albera Italy 18 79 0.4× 541 3.0× 430 3.2× 5 0.1× 202 2.9× 35 781
Garth Tarr Australia 12 27 0.1× 42 0.2× 174 1.3× 60 0.6× 22 0.3× 32 366
Chong He United States 14 426 1.9× 66 0.4× 15 0.1× 68 0.7× 6 0.1× 31 689
Lars Gidskehaug Norway 11 164 0.7× 270 1.5× 57 0.4× 13 0.1× 16 0.2× 13 538
Nick V. L. Serão United States 22 386 1.7× 541 3.0× 585 4.4× 7 0.1× 327 4.7× 96 1.5k
Shaokang Chen China 10 168 0.8× 158 0.9× 121 0.9× 2 0.0× 21 0.3× 14 371

Countries citing papers authored by David Causeur

Since Specialization
Citations

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

Fields of papers citing papers by David Causeur

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Causeur

This figure shows the co-authorship network connecting the top 25 collaborators of David Causeur. A scholar is included among the top collaborators of David Causeur 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 David Causeur. David Causeur 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.
Wingenbach, Tanja S. H., et al.. (2021). Implicit responses in the judgment of attractiveness in faces with differing levels of makeup.. Psychology of Aesthetics Creativity and the Arts. 17(1). 29–42. 2 indexed citations
2.
Causeur, David, Julien Jardin, Valérie Briard‐Bion, et al.. (2021). Statistical modeling of in vitro pepsin specificity. Food Chemistry. 362. 130098–130098. 19 indexed citations
3.
Prado, Énora, David Causeur, Mathilde Dupont‐Nivet, et al.. (2021). Prediction of fatty acids composition in the rainbow trout Oncorhynchus mykiss by using Raman micro-spectroscopy. Analytica Chimica Acta. 1191. 339212–339212. 5 indexed citations
4.
Kloareg, Maëla & David Causeur. (2021). Double Sampling Designs to Reduce the Non-discovery Rate: Application to Microarray Data. Journal of Data Science. 7(2). 219–234.
5.
Haffray, Pierrick, Énora Prado, Nicolas Dechamp, et al.. (2021). Genetic architecture and genomic selection of fatty acid composition predicted by Raman spectroscopy in rainbow trout. BMC Genomics. 22(1). 788–788. 14 indexed citations
6.
7.
Gondret, Florence, Annie Vincent, Anne Siegel, et al.. (2017). A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs. BMC Genomics. 18(1). 244–244. 75 indexed citations
8.
Gondret, Florence, Annie Vincent, Anne Siegel, et al.. (2016). Molecular alterations induced by a high-fat high-fiber diet in porcine adipose tissues: variations according to the anatomical fat location. BMC Genomics. 17(1). 120–120. 15 indexed citations
9.
Hornung, Roman, Anne‐Laure Boulesteix, & David Causeur. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics. 17(1). 27–27. 35 indexed citations
10.
Perthame, Émeline, Chloé Friguet, & David Causeur. (2015). Stability of feature selection in classification issues for high-dimensional correlated data. Statistics and Computing. 26(4). 783–796. 16 indexed citations
11.
Haziza, David, et al.. (2014). Preserving relationships between variables with MIVQUE based imputation for missing survey data. Journal of Multivariate Analysis. 131. 197–208. 3 indexed citations
12.
Mach, Núria, Yuna Blum, A. Bannink, et al.. (2012). Pleiotropic effects of polymorphism of the gene diacylglycerol-O-transferase 1 (DGAT1) in the mammary gland tissue of dairy cows. Journal of Dairy Science. 95(9). 4989–5000. 16 indexed citations
13.
Blum, Yuna, Guillaume Le Mignon, David Causeur, et al.. (2011). Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken. BMC Genomics. 12(1). 567–567. 3 indexed citations
14.
Friguet, Chloé & David Causeur. (2011). Estimation of the proportion of true null hypotheses in high-dimensional data under dependence. Computational Statistics & Data Analysis. 55(9). 2665–2676. 11 indexed citations
15.
Caffier, Valérie, et al.. (2010). Aggressiveness of eight Venturia inaequalis isolates virulent or avirulent to the major resistance gene Rvi6 on a non‐ Rvi6 apple cultivar. Plant Pathology. 59(6). 1072–1080. 26 indexed citations
16.
17.
Cutullic, Erwan, et al.. (2008). Hierarchy of factors affecting behavioural signs used for oestrus detection of Holstein and Normande dairy cows in a seasonal calving system. Animal Reproduction Science. 113(1-4). 22–37. 46 indexed citations
18.
Causeur, David, et al.. (2004). A two-way analysis of variance model with positive definite interaction for homologous factors. Journal of Multivariate Analysis. 95(2). 431–448. 2 indexed citations
19.
Causeur, David & François Husson. (2004). A 2-dimensional extension of the Bradley–Terry model for paired comparisons. Journal of Statistical Planning and Inference. 135(2). 245–259. 15 indexed citations
20.
Causeur, David, et al.. (2003). Linear Regression Models under Conditional Independence Restrictions. Scandinavian Journal of Statistics. 30(3). 637–650. 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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