Ghislain Durif
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View article: The Fate of a Polygenic Phenotype Within the Genomic Landscapes of Introgression in the European Seabass Hybrid Zone
The Fate of a Polygenic Phenotype Within the Genomic Landscapes of Introgression in the European Seabass Hybrid Zone Open
Unraveling the evolutionary mechanisms and consequences of hybridization is a major concern in biology. Many studies have documented the interplay between recombination and selection in modulating the genomic landscape of introgression, bu…
View article: Nonparametric tests and funStatTest R package for functional data
Nonparametric tests and funStatTest R package for functional data Open
International audience
View article: Kernel-Based Testing for Single-Cell Differential Analysis
Kernel-Based Testing for Single-Cell Differential Analysis Open
Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigen…
View article: Benchopt : Reproducible, efficient and collaborative optimization benchmarks
Benchopt : Reproducible, efficient and collaborative optimization benchmarks Open
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several chall…
View article: Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Benchopt: Reproducible, efficient and collaborative optimization benchmarks Open
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several chall…
View article: Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest
Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest Open
Simulation‐based methods such as approximate Bayesian computation (ABC) are well‐adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide at…
View article: Extending Approximate Bayesian Computation with Supervised Machine Learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest
Extending Approximate Bayesian Computation with Supervised Machine Learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest Open
Simulation-based methods such as Approximate Bayesian Computation (ABC) are well adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide at…
View article: Probabilistic count matrix factorization for single cell expression data analysis
Probabilistic count matrix factorization for single cell expression data analysis Open
Motivation The development of high-throughput single-cell sequencing technologies now allows the investigation of the population diversity of cellular transcriptomes. The expression dynamics (gene-to-gene variability) can be quantified mor…
View article: Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis
Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis Open
The development of high throughput single-cell sequencing technologies now allows the investigation of the population level diversity of cellular transcriptomes. This diversity has shown two faces. First, the expression dynamics (gene to g…
View article: High dimensional classification with combined adaptive sparse PLS and logistic regression
High dimensional classification with combined adaptive sparse PLS and logistic regression Open
Motivation The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over-optimistic predictions. This challenge can be tackled by compression and variable selection, …
View article: Multivariate analysis of high-throughput sequencing data
Multivariate analysis of high-throughput sequencing data Open
The statistical analysis of Next-Generation Sequencing data raises many computational challenges regarding modeling and inference, especially because of the high dimensionality of genomic data. The research work in this manuscript concerns…
View article: Adaptive Sparse PLS for Logistic Regression
Adaptive Sparse PLS for Logistic Regression Open
For a few years, data analysis has been struggling with statistical issues related to the curse of high dimensionality. In this context, i.e. when the number of considered variables is far larger than the number of observations in the samp…