Prediction-oriented prognostic biomarker discovery with survival machine learning methods.

ABSTRACT: Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting. The performances of these feature selection methods when applied to survival models, on the other hand, deserve further investigation. In this paper, we construct and compare a series of prediction-oriented biomarker selection frameworks by leveraging recent machine learning algorithms, including random survival forests, extreme g

SUBMITTER: Yao S 

PROVIDER: S-EPMC10273194 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature