Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107512/1/biom12137-sm-0001-SuppData.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/107512/2/biom12137.pd
Modern biomedical studies generate high-dimensional data, meaning that the number of variables colle...
Abstract Background Building prognostic models of clinical outcomes is an increasingly important res...
A large number of cancer drugs have been developed to target particular genes/pathways that are cruc...
Statistical challenges arise from modern biomedical studies that produce time course genomic data wi...
AbstractIt is rather challenging for current variable selectors to handle situations where the numbe...
We propose a new methodology to select and rank covariates associated to avariable of interest in...
National audienceOver the last decades, molecular signatures have become increasingly important in o...
Use of microarray technology often leads to high-dimensional and low- sample size data settings. Ov...
We propose a new methodology for selecting and ranking covariates associated with a variable of inte...
This article considers the problem of selecting predictors of time to an event from a high-dimension...
Abstract Background Feature selection is commonly employed for identifying collectively-predictive b...
High-dimensional data are commonly seen in modern statistical applications, variable selection metho...
AbstractAnalysis of microarray data is associated with the methodological problems of high dimension...
Nowadays it is common to collect large volumes of data in many fields with an extensive amount of va...
Identifying important biomarkers that are predictive for cancer patients\u27 prognosis is key in ga...
Modern biomedical studies generate high-dimensional data, meaning that the number of variables colle...
Abstract Background Building prognostic models of clinical outcomes is an increasingly important res...
A large number of cancer drugs have been developed to target particular genes/pathways that are cruc...
Statistical challenges arise from modern biomedical studies that produce time course genomic data wi...
AbstractIt is rather challenging for current variable selectors to handle situations where the numbe...
We propose a new methodology to select and rank covariates associated to avariable of interest in...
National audienceOver the last decades, molecular signatures have become increasingly important in o...
Use of microarray technology often leads to high-dimensional and low- sample size data settings. Ov...
We propose a new methodology for selecting and ranking covariates associated with a variable of inte...
This article considers the problem of selecting predictors of time to an event from a high-dimension...
Abstract Background Feature selection is commonly employed for identifying collectively-predictive b...
High-dimensional data are commonly seen in modern statistical applications, variable selection metho...
AbstractAnalysis of microarray data is associated with the methodological problems of high dimension...
Nowadays it is common to collect large volumes of data in many fields with an extensive amount of va...
Identifying important biomarkers that are predictive for cancer patients\u27 prognosis is key in ga...
Modern biomedical studies generate high-dimensional data, meaning that the number of variables colle...
Abstract Background Building prognostic models of clinical outcomes is an increasingly important res...
A large number of cancer drugs have been developed to target particular genes/pathways that are cruc...
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