Publication date
January 2019
DOIAbstract
I-Boost is a statistical boosting method that integrates multiple types of high-dimensional genomics data with clinical data for predicting survival time
I-Boost is a statistical boosting method that integrates multiple types of high-dimensional genomics data with clinical data for predicting survival time
There are several techniques for fitting sparse survival models to high-dimensional data, arising e....
Gene-gene interactions have long been recognized to be fundamentally important for understanding gen...
Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for ca...
This contains the simulation data sets and codes to perform all the analyses in the paper: Wong KY, ...
This contains the simulation data sets and codes to perform all the analyses in the paper: Wong KY, ...
We propose a statistical boosting method, termed I-Boost, to integrate multiple types of high-dimens...
This data set contains the clinical and genomics data for 1,420 subjects analyzed in the paper: Wong...
We present a statistical perspective on boosting. Special emphasis is given to estimating potentiall...
Supplemental material, sj-docx-1-cic-10.1177_11795468221133611 for XGBoost, A Novel Explainable AI T...
In biomedical research, boosting-based regression approaches have gained much attention in the last ...
This work presents a novel method for applying test-time augmentation (TTA) to tabular data. We used...
Motivation: An important area of research in the postgenomics era is to relate high-dimensional gene...
Motivation: Microarray experiments are expected to contribute significantly to the progress in cance...
Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) fo...
Cancer is one of the most deadly diseases that the world has been fighting against over dec...
There are several techniques for fitting sparse survival models to high-dimensional data, arising e....
Gene-gene interactions have long been recognized to be fundamentally important for understanding gen...
Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for ca...
This contains the simulation data sets and codes to perform all the analyses in the paper: Wong KY, ...
This contains the simulation data sets and codes to perform all the analyses in the paper: Wong KY, ...
We propose a statistical boosting method, termed I-Boost, to integrate multiple types of high-dimens...
This data set contains the clinical and genomics data for 1,420 subjects analyzed in the paper: Wong...
We present a statistical perspective on boosting. Special emphasis is given to estimating potentiall...
Supplemental material, sj-docx-1-cic-10.1177_11795468221133611 for XGBoost, A Novel Explainable AI T...
In biomedical research, boosting-based regression approaches have gained much attention in the last ...
This work presents a novel method for applying test-time augmentation (TTA) to tabular data. We used...
Motivation: An important area of research in the postgenomics era is to relate high-dimensional gene...
Motivation: Microarray experiments are expected to contribute significantly to the progress in cance...
Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) fo...
Cancer is one of the most deadly diseases that the world has been fighting against over dec...
There are several techniques for fitting sparse survival models to high-dimensional data, arising e....
Gene-gene interactions have long been recognized to be fundamentally important for understanding gen...
Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for ca...
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