ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2018
Robust sparse accelerated failure time model for survival analysis.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Genetic insights into the timing of metastasis: a secondary analysis of the Count Me In metastatic breast cancer cohort.Frontiers in cell and developmental biology · 2026Article
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9 authors.
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Abstract
To identify the bio-mark genes related to disease with high dimension and low sample size gene expression data, various regression approaches with different regularization methods have been proposed to solve this problem. Nevertheless, high-noises in biological data significantly reduce the performances of methods. The accelerated failure time (AFT) modelwas designed for gene selection and survival time estimation in cancer survival analysis. In this article, we proposed a novel robust sparse accelerated failure time model (RS-AFT) through combining the least absolute deviation (LAD) and Lq regularization. An iterative weighted linear programming algorithm without regularization parameter tuning was proposed to solve this RS-AFT model. The results of the experiments show our method has better performancebothin gene selection and survival time estimationthan some widely used regularization methods such as lasso, elastic net and SCAD. Hence we thought the RS-AFT model may be a competitive regularization method in cancer survival analysis.
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