Apr 18, 2018

Pathway aggregation for survival prediction via multiple kernel learning

Statistics in Medicine
Jennifer A Sinnott, Tianxi Cai

Abstract

Attempts to predict prognosis in cancer patients using high-dimensional genomic data such as gene expression in tumor tissue can be made difficult by the large number of features and the potential complexity of the relationship between features and the outcome. Integrating prior biological knowledge into risk prediction with such data by grouping genomic features into pathways and networks reduces the dimensionality of the problem and could improve prediction accuracy. Additionally, such knowledge-based models may be more biologically grounded and interpretable. Prediction could potentially be further improved by allowing for complex nonlinear pathway effects. The kernel machine framework has been proposed as an effective approach for modeling the nonlinear and interactive effects of genes in pathways for both censored and noncensored outcomes. When multiple pathways are under consideration, one may efficiently select informative pathways and aggregate their signals via multiple kernel learning (MKL), which has been proposed for prediction of noncensored outcomes. In this paper, we propose MKL methods for censored survival outcomes. We derive our approach for a general survival modeling framework with a convex objective functio...Continue Reading

  • References20
  • Citations4

References

Mentioned in this Paper

Study
Biochemical Pathway
Genome
Genes
Tumor Tissue Sample
Aggregation
MKL/Myocardin-Like Protein 1
Gene Expression
Genomics
Learning

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