Decentralized Learning Framework of Meta-Survival Analysis for Developing Robust Prognostic Signatures.

ABSTRACT:

Purpose

A significant hurdle in developing reliable gene expression-based prognostic models has been the limited sample size, which can cause overfitting and false discovery. Combining data from multiple studies can enhance statistical power and reduce spurious findings, but how to address the biologic heterogeneity across different datasets remains a major challenge. Better meta-survival analysis approaches are needed.

Material and methods

We presented a decentralized learning framework for meta-survival analysis without the need for data aggregation. Our method consisted of a series of proposals that together alleviated the influence of data heterogeneity and improved the performance of survival prediction. First, we transformed the gene expression profile of every sample

SUBMITTER: Cui Y 

PROVIDER: S-EPMC6873986 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature