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Related Experiment Video

Updated: Oct 8, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Efficient gradient boosting for prognostic biomarker discovery.

Kaiqiao Li¹, Sijie Yao², Zhenyu Zhang¹

¹Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.

Bioinformatics (Oxford, England)

|January 3, 2022

Summary

This summary is machine-generated.

We introduce Xsurv, an R package using modern gradient boosting decision tree (GBDT) methods like XGBoost and LightGBM for biomarker discovery in cancer survival data. This tool efficiently identifies prognostic biomarkers for censored outcomes.

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Area of Science:

Computational Biology
Machine Learning
Bioinformatics

Background:

Gradient Boosting Decision Trees (GBDTs) are powerful for high-dimensional biomarker discovery.
Modern GBDT algorithms (XGBoost, LightGBM) offer improved efficiency and accuracy.
These advanced methods are underutilized for discovering biomarkers associated with censored survival outcomes in cancer research.

Purpose of the Study:

To present 'Xsurv', an R package integrating XGBoost and LightGBM for modeling right-censored survival data.
To provide a computational tool for efficient biomarker discovery in cancer studies.
To facilitate the identification of prognostic candidate biomarkers for translational research.

Main Methods:

Developed the 'Xsurv' R package implementing XGBoost and LightGBM for survival outcome analysis.
Conducted simulation studies to benchmark 'Xsurv' against traditional methods (e.g., Cox regression, 'gbm' package).
Applied 'Xsurv' to analyze a real-world melanoma methylation dataset.

Main Results:

Simulations demonstrated the performance of XGBoost and LightGBM for censored survival outcomes.
The 'Xsurv' package provides a computationally viable solution for biomarker screening.
Analysis of the melanoma dataset showcased the practical application of 'Xsurv' in identifying potential prognostic biomarkers.

Conclusions:

'Xsurv' is a valuable tool for accelerating biomarker discovery from high-dimensional molecular data in cancer.
The package enables efficient screening of prognostic candidate biomarkers for censored survival endpoints.
Facilitates future translational and clinical research by providing accessible and modern machine learning methods.