Efficient gradient boosting for prognostic biomarker discovery

Kaiqiao Li, Sijie Yao, Zhenyu Zhang, Biwei Cao, Christopher M. Wilson, Denise Kalos, Pei Fen Kuan, Ruoqing Zhu, Xuefeng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: A gradient boosting decision tree (GBDT) is a powerful ensemble machine-learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as extreme gradient boosting (XGB) and light gradient boosting (LGB), have rendered the GBDT training more efficient, scalable and accurate. However, these modern techniques have not yet been widely adopted in discovering biomarkers for censored survival outcomes, which are key clinical outcomes or endpoints in cancer studies. Results: In this paper, we present a new R package 'Xsurv' as an integrated solution that applies two modern GBDT training frameworks namely, XGB and LGB, for the modeling of right-censored survival outcomes. Based on our simulations, we benchmark the new approaches against traditional methods including the stepwise Cox regression model and the original gradient boosting function implemented in the package 'gbm'. We also demonstrate the application of Xsurv in analyzing a melanoma methylation dataset. Together, these results suggest that Xsurv is a useful and computationally viable tool for screening a large number of prognostic candidate biomarkers, which may facilitate future translational and clinical research.

Original languageEnglish (US)
Pages (from-to)1631-1638
Number of pages8
JournalBioinformatics
Volume38
Issue number6
DOIs
StatePublished - Mar 15 2022

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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