Benoît Liquet
YOU?
Author Swipe
View article: Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels
Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels Open
Building spatial process models that capture nonstationary behavior while delivering computationally efficient inference is challenging. Nonstationary spatially varying kernels (see, e.g., Paciorek, 2003) offer flexibility and richness, bu…
View article: HEXB Drives Raised Paucimannosylation in Colorectal Cancer and Stratifies Patient Risk
HEXB Drives Raised Paucimannosylation in Colorectal Cancer and Stratifies Patient Risk Open
Noninvasive prognostic markers are needed to improve the survival of colorectal cancer (CRC) patients. Toward this goal, we applied untargeted systems glycobiology approaches to snap-frozen and formalin-fixed paraffin-embedded tumor tissue…
View article: Meta-analysis models with group structure for pleiotropy detection at gene and variant level using summary statistics from multiple datasets
Meta-analysis models with group structure for pleiotropy detection at gene and variant level using summary statistics from multiple datasets Open
Summary Genome-wide association studies (GWASs) have highlighted the importance of pleiotropy in human diseases, where one gene can impact 2 or more unrelated traits. Examining shared genetic risk factors across multiple diseases can enhan…
View article: Deep learning-based hyperspectral image correction and unmixing for brain tumor surgery
Deep learning-based hyperspectral image correction and unmixing for brain tumor surgery Open
Hyperspectral imaging for fluorescence-guided brain tumor resection improves visualization of tissue differences, which can ameliorate patient outcomes. However, current methods do not effectively correct for heterogeneous optical and geom…
View article: Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors
Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors Open
Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled the detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a mi…
View article: Abstract Journal Pain Medicine & Surgery
Abstract Journal Pain Medicine & Surgery Open
View article: Group COMBSS: Group Selection via Continuous Optimization
Group COMBSS: Group Selection via Continuous Optimization Open
We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is to select a small set of groups that best fits the respons…
View article: Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization
Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization Open
The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high dimensional contexts where the number of variables is usually much larger than the number of observations. In this paper, …
View article: Dealing with area‐to‐point spatial misalignment in species distribution models
Dealing with area‐to‐point spatial misalignment in species distribution models Open
Species distribution models (SDMs) are extensively used to estimate species–environment relationships (SERs) and predict species distribution across space and time. For this purpose, it is key to choose relevant spatial grains for predicto…
View article: A maximum penalised likelihood approach for semiparametric accelerated failure time models with time-varying covariates and partly interval censoring
A maximum penalised likelihood approach for semiparametric accelerated failure time models with time-varying covariates and partly interval censoring Open
Accelerated failure time (AFT) models are frequently used to model survival data, providing a direct quantification of the relationship between event times and covariates. These models allow for the acceleration or deceleration of failure …
View article: Spatial Autoregressive Model on a Dirichlet Distribution
Spatial Autoregressive Model on a Dirichlet Distribution Open
Compositional data find broad application across diverse fields due to their efficacy in representing proportions or percentages of various components within a whole. Spatial dependencies often exist in compositional data, particularly whe…
View article: COMBSS: best subset selection via continuous optimization
COMBSS: best subset selection via continuous optimization Open
The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly challenging when the total available number of features is very …
View article: The SAMI galaxy survey: predicting kinematic morphology with logistic regression
The SAMI galaxy survey: predicting kinematic morphology with logistic regression Open
We use the SAMI (Sydney-AAO Multi-object Integral field spectrograph) galaxy survey to study the the kinematic morphology–density relation: the observation that the fraction of slow rotator galaxies increases towards dense environments. We…
View article: Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery Open
Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient …
View article: The SAMI galaxy survey: predicting kinematic morphology with logistic regression
The SAMI galaxy survey: predicting kinematic morphology with logistic regression Open
We use the SAMI galaxy survey to study the the kinematic morphology-density relation: the observation that the fraction of slow rotator galaxies increases towards dense environments. We build a logistic regression model to quantitatively s…
View article: A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors
A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors Open
Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a minima…
View article: Hydrocortisone vs. dexamethasone in the prophylaxis of post-subarachnoid hemorrhage cerebral salt wasting syndrome
Hydrocortisone vs. dexamethasone in the prophylaxis of post-subarachnoid hemorrhage cerebral salt wasting syndrome Open
View article: A deep learning super-resolution model to speed up computations of coastal sea states
A deep learning super-resolution model to speed up computations of coastal sea states Open
View article: Investigation of common genetic risk factors between thyroid traits and breast cancer
Investigation of common genetic risk factors between thyroid traits and breast cancer Open
Breast cancer (BC) risk is suspected to be linked to thyroid disorders, however observational studies exploring the association between BC and thyroid disorders gave conflicting results. We proposed an alternative approach by investigating…
View article: GCPBayes: An R package for studying Cross-Phenotype Genetic Associations with Group-level Bayesian Meta-Analysis
GCPBayes: An R package for studying Cross-Phenotype Genetic Associations with Group-level Bayesian Meta-Analysis Open
International audience
View article: GCPBayes pipeline: a tool for exploring pleiotropy at the gene level
GCPBayes pipeline: a tool for exploring pleiotropy at the gene level Open
Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is…
View article: Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data
Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data Open
Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved unders…
View article: SMOTE-CD: SMOTE for compositional data
SMOTE-CD: SMOTE for compositional data Open
Compositional data are a special kind of data, represented as a proportion carrying relative information. Although this type of data is widely spread, no solution exists to deal with the cases where the classes are not well balanced. After…
View article: COMBSS: Best Subset Selection via Continuous Optimization
COMBSS: Best Subset Selection via Continuous Optimization Open
The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly challenging when the total available number of features is very …
View article: In-situ measurements of energetic depth-limited wave loading
In-situ measurements of energetic depth-limited wave loading Open
View article: COMBSS: Best Subset Selection via Continuous Optimization
COMBSS: Best Subset Selection via Continuous Optimization Open
The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly challenging when the total available number of features is very …
View article: Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels
Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels Open
Building spatial process models that capture nonstationary behavior while delivering computationally efficient inference is challenging. Nonstationary spatially varying kernels (see, e.g., Paciorek, 2003) offer flexibility and richness, bu…
View article: Leveraging pleiotropic association using sparse group variable selection in genomics data
Leveraging pleiotropic association using sparse group variable selection in genomics data Open
View article: Central subspaces review: methods and applications
Central subspaces review: methods and applications Open
Central subspaces have long been a key concept for sufficient dimension reduction. Initially constructed for solving problems in the p < n setting, central subspace methods have seen many successes and developments. However, over the last …
View article: Multi-Index Ecoacoustics Analysis for Terrestrial Soundscapes: A New Semi-Automated Approach Using Time-Series Motif Discovery and Random Forest Classification
Multi-Index Ecoacoustics Analysis for Terrestrial Soundscapes: A New Semi-Automated Approach Using Time-Series Motif Discovery and Random Forest Classification Open
High rates of biodiversity loss caused by human-induced changes in the environment require new methods for large scale fauna monitoring and data analysis. While ecoacoustic monitoring is increasingly being used and shows promise, analysis …