Benoît Liquet

2.8k total citations
90 papers, 1.4k citations indexed

About

Benoît Liquet is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Benoît Liquet has authored 90 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Statistics and Probability, 16 papers in Artificial Intelligence and 15 papers in Molecular Biology. Recurrent topics in Benoît Liquet's work include Statistical Methods and Inference (28 papers), Statistical Methods and Bayesian Inference (15 papers) and Gene expression and cancer classification (10 papers). Benoît Liquet is often cited by papers focused on Statistical Methods and Inference (28 papers), Statistical Methods and Bayesian Inference (15 papers) and Gene expression and cancer classification (10 papers). Benoît Liquet collaborates with scholars based in France, Australia and United Kingdom. Benoît Liquet's co-authors include Marie Chavent, Jérôme Saracco, Vanessa Kuentz-Simonet, Daniel Commenges, Rodolphe Thiébaut, Pierre Lafaye de Micheaux, Denis Morichon, Stéphane Abadie, Matthias Delpey and Kim‐Anh Lê Cao and has published in prestigious journals such as Environmental Science & Technology, Bioinformatics and PLoS ONE.

In The Last Decade

Benoît Liquet

82 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Benoît Liquet France 18 265 190 171 127 114 90 1.4k
Garrett Grolemund United States 8 84 0.3× 152 0.8× 177 1.0× 317 2.5× 67 0.6× 9 1.6k
Andrew A. Neath United States 11 550 2.1× 125 0.7× 392 2.3× 86 0.7× 47 0.4× 31 2.3k
Victor Hasselblad United States 30 237 0.9× 124 0.7× 244 1.4× 109 0.9× 283 2.5× 60 2.6k
Avner Bar‐Hen France 27 127 0.5× 260 1.4× 197 1.2× 290 2.3× 44 0.4× 110 2.2k
Gerhard Tutz Germany 9 417 1.6× 162 0.9× 372 2.2× 216 1.7× 45 0.4× 16 2.6k
Mark Greenwood United States 22 355 1.3× 293 1.5× 176 1.0× 336 2.6× 28 0.2× 55 1.9k
Yoshitaka Sakamoto Japan 16 168 0.6× 561 3.0× 150 0.9× 326 2.6× 81 0.7× 42 2.3k
Makio Ishiguro Japan 12 260 1.0× 156 0.8× 297 1.7× 317 2.5× 33 0.3× 52 2.3k
J. P. Royston United Kingdom 24 343 1.3× 166 0.9× 137 0.8× 145 1.1× 77 0.7× 59 2.8k
Gary W. Oehlert United States 16 226 0.9× 140 0.7× 93 0.5× 345 2.7× 68 0.6× 42 2.2k

Countries citing papers authored by Benoît Liquet

Since Specialization
Citations

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

Fields of papers citing papers by Benoît Liquet

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Benoît Liquet

This figure shows the co-authorship network connecting the top 25 collaborators of Benoît Liquet. A scholar is included among the top collaborators of Benoît Liquet 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 Benoît Liquet. Benoît Liquet 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.
Banerjee, Sudipto, et al.. (2025). Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels. Journal of Computational and Graphical Statistics. 35(1). 173–183.
2.
Liquet, Benoît, et al.. (2024). Abstract Journal Pain Medicine & Surgery. ANZ Journal of Surgery. 94(S1). 145–146. 1 indexed citations
3.
Liquet, Benoît, et al.. (2024). Mathematical Engineering of Deep Learning. 15 indexed citations
4.
Black, David, Benoît Liquet, Antonio Di Ieva, Walter Stummer, & Eric Suero Molina. (2024). Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors. Biomedical Optics Express. 15(8). 4406–4406. 5 indexed citations
5.
Abadie, Stéphane, et al.. (2023). A deep learning super-resolution model to speed up computations of coastal sea states. Applied Ocean Research. 141. 103776–103776. 9 indexed citations
6.
Asgari, Yazdan, et al.. (2023). GCPBayes pipeline: a tool for exploring pleiotropy at the gene level. NAR Genomics and Bioinformatics. 5(3). lqad065–lqad065. 1 indexed citations
7.
Asgari, Yazdan, Pierre‐Emmanuel Sugier, Fabienne Lesueur, et al.. (2023). Investigation of common genetic risk factors between thyroid traits and breast cancer. Human Molecular Genetics. 33(1). 38–47. 1 indexed citations
8.
Liquet, Benoît, Kerrie Mengersen, Erin E. Peterson, et al.. (2023). Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data. PLoS ONE. 18(6). e0287640–e0287640. 8 indexed citations
9.
Mengersen, Kerrie, et al.. (2023). SMOTE-CD: SMOTE for compositional data. PLoS ONE. 18(6). e0287705–e0287705. 6 indexed citations
10.
Liquet, Benoît, Jeremy B. Jones, Kerrie Mengersen, et al.. (2021). Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters. International Journal of Environmental Research and Public Health. 18(23). 12803–12803. 6 indexed citations
11.
Russo, Carlo, et al.. (2021). Spatial and time domain analysis of eye-tracking data during screening of brain magnetic resonance images. PLoS ONE. 16(12). e0260717–e0260717. 8 indexed citations
12.
Liquet, Benoît, et al.. (2020). Estimation of Semi-Markov Multi-state Models: A Comparison of the\n Sojourn Times and Transition Intensities Approaches. arXiv (Cornell University). 13 indexed citations
13.
Vercelloni, Julie, Benoît Liquet, Emma Kennedy, et al.. (2020). Forecasting intensifying disturbance effects on coral reefs. Global Change Biology. 26(5). 2785–2797. 50 indexed citations
14.
Liquet, Benoît & Jérémie Riou. (2019). CPMCGLM: an R package for p-value adjustment when looking for an optimal transformation of a single explanatory variable in generalized linear models. BMC Medical Research Methodology. 19(1). 79–79. 4 indexed citations
15.
Liquet, Benoît, et al.. (2017). CEoptim: cross-entropy R package for optimization. RePEc: Research Papers in Economics. 1 indexed citations
16.
Commenges, Daniel, Cécile Proust‐Lima, Cécilia Samieri, & Benoît Liquet. (2015). A Universal Approximate Cross-Validation Criterion for Regular Risk Functions. The International Journal of Biostatistics. 11(1). 51–67. 6 indexed citations
17.
Truong, Thérèse, Benoît Liquet, F. Ménégaux, et al.. (2014). Breast cancer risk, nightwork, and circadian clock gene polymorphisms. Endocrine Related Cancer. 21(4). 629–638. 65 indexed citations
18.
Liquet, Benoît, Kim‐Anh Lê Cao, Hakim Hocini, & Rodolphe Thiébaut. (2012). A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC Bioinformatics. 13(1). 325–325. 93 indexed citations
19.
Liquet, Benoît, Jean-François Timsit, & Virginie Rondeau. (2012). Investigating hospital heterogeneity with a multi-state frailty model: application to nosocomial pneumonia disease in intensive care units. BMC Medical Research Methodology. 12(1). 79–79. 23 indexed citations
20.
Liquet, Benoît & Daniel Commenges. (2004). Estimating the Expectation of the Log-Likelihood with Censored Data for Estimator Selection. Lifetime Data Analysis. 10(4). 351–367. 3 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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