PrePrints

• Estimating relapse time distribution from longitudinal biomarker trajectories using iterative regression and continuous time Markov processes, Alice Cleynen, Benoîte de Saporta, Amélie Vernay, arXiv:2503.10448
• Bridging Impulse Control of Piecewise Deterministic Markov Processes and Markov Decision Processes: Frameworks, Extensions, and Open Challenges, Alice Cleynen, Benoîte de Saporta, Orlane Rossini, Régis Sabbadin, Amélie Vernay, arXiv:2501.04120
• Bayes-Adaptive Impulse Control of Piecewise-Deterministic Markov Processes, Orlane Rossini, Meritxell Vinyals, Alice Cleynen, Benoîte de Saporta, Régis Sabbadin
• Sequential decision making for a class of hidden Markov processes, application to medical treatment optimisation, Alice Cleynen and Benoîte de Saporta, arXiv :2112.09408
• Unsupervized identification of prognostic copy-number alterations using segmentation and lasso regularization, Cleynen et.al https://doi.org/10.1101/2022.12.14.520497
• High-Accuracy RNA Integrity Definition for Unbiased Transcriptome Comparisons with INDEGRA, Cleynen et. al. https://doi.org/10.1101/2024.12.12.627949

Published

• [22] A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data:  PLOS ONE, de Saporta et.al. 2024 https://doi.org/10.1371/journal.pone.0315661 
• [21] Comprehensive translational profiling and STE AI uncover rapid control of protein biosynthesis during cell stress:  Nucleic Acid Research, Horvath et.al. 2024 https://doi.org/10.1093/nar/gkae365 
• [20] Local tree methods for classification:  Computo, Cleynen et.al. 2023 ISSN 2824-7795
• [19] In multiple myeloma, high-risk secondary genetic events observed at relapse are present from diagnosis in tiny, undetectable subclonal populations:  Journal of Clinical Oncology, Lannes et.al. 2022 https://doi.org/10.1200/JCO.21.01987
• [18] Evaluation of mineralogy per geological layers by Approximate Bayesian Computation: SPE Journal, Vianney Bruned et. al.2020, https://doi.org/10.2118/201187-PA
• [17] Variable BCL2/BCL2L1 ratio in multiple myeloma with t(11;14) : Blood Letter, Cleynen et al 2018, https://doi.org/10.1182/blood-2018-09-876433
  
• [16] Change-point detection for piecewise deterministic Markov processes : Automatica, Cleynen A. and de Saporta B. 2018 https://doi.org/10.1016/j.automatica.2018.08.011
• [15] Multiple myeloma clonal evolution in homogeneously treated patients : Leukemia, Corre et.al. 2018 https://doi.org/10.1038/s41375-018-0153-6
• [14] Long Intergenic Non-Coding RNAs have an Independent Impact on Survival in Multiple Myeloma : Leukemia, Samur et.al. 2018 https://doi.org/10.1038/s41375-018-0116-y
• [13] Dysregulated IL-18 is a key driver of immunosuppression and a possible therapeutic target in the multiple myeloma microenvironment : Cancer Cell, Nakamura et.al. 2018 https://doi.org/10.1016/j.ccell.2018.02.007
• [12] Expressed fusion gene landscape and its impact in Multiple Myeloma : Nature Communications, Cleynen A. et. al.  2017 https://doi:10.1038/s41467-017-00638-w [pdf]
• [11] Nucleotide excision repair is a potential therapeutic target in multiple myeloma : Leukemia, Szalat et. al. 2017 https://doi.org/10.1038/leu.2017.182
• [10] Model selection for the segmentation of multiparameter exponential family distributions :  Electronic Journal of Statistics, Cleynen A. and Lebarbier E. 2017 https://doi.org/10.1214/17-EJS1246

• [9] Genomics of Multiple Myeloma : Journal of Clinical Oncology, Robiou du Pont et. al. 2016 http://dx.doi.org/10.1200/JCO.2016.70.6705

• [8] Understanding the role of hyperdiploidy in myeloma prognosis: which trisomies really matter? : Blood Chrétien et. al. 2015 http://dx.doi.org/10.1182/blood-2015-06-650242

• [7] Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience: Blood Hebraud et. al. 2015 http://dx.doi.org/10.1182/blood-2014-07-587964

• [6] Inference in finite state space non parametric Hidden Markov Models and applications:  Statistics and Computing Gassiat E., Cleynen A., Robin S. 2014 http://dx.doi.org/10.1007/s11222-014-9523-8 [pdf]

• [5] Comparing change-point locations of independent profiles: Statistics and Computing Cleynen A., Robin S. 2014 http://dx.doi.org/10.1007/s11222-014-9492-y  [pdf]
  

• [4] Segmentation of the Poisson and negative binomial rate models: a penalized estimator: ESAIM, P&S  Cleynen A., Lebarbier E. 2014 http://dx.doi.org/10.1051/ps/2014005 [pdf]

• [3] Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data:  Algorithms for Molecular Biology Cleynen A., Koskas M., Lebarbier E., Rigaill G., Robin S. 2014 http://dx.doi.org/10.1186/1748-7188-9-6 [pdf]

• [2] Fast estimation of the Integrated Completed Likelihood criterion for change-point detection problems with applications to Next-Generation Sequencing data: Signal Processing,  Cleynen A., Luong T.M., Rigaill G., Nuel G. 2013 http://dx.doi.org/10.1016/j.sigpro.2013.11.029 [pdf]

• [1] Comparing Segmentation Methods for Genome Annotation Based on RNA-Seq Data: Journal of Agricultural, Biological, and Environmental Statistics, Cleynen, A., Dudoit, S., Robin, S.  2013 http://dx.doi.org/10.1007/s13253-013-0159-5 [pdf]

Thesis

• My HDR manuscript (in English) can be downloaded here

• My PhD manuscript (in English) can be downloaded here