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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Related Experiment Video

Updated: Jun 19, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Can survival prediction be improved by merging gene expression data sets?

Haleh Yasrebi1, Peter Sperisen, Viviane Praz

  • 1Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology (EPFL), School of Life Sciences, EPFL SV ISREC, Lausanne, Switzerland. Haleh.Yasrebi@epfl.ch

Plos One
|October 24, 2009
PubMed
Summary

Merging gene expression data from multiple breast cancer studies did not significantly improve survival prediction accuracy. However, predictors from merged datasets were more robust and reproducible, identifying CYB5D1 as a strong survival factor.

Related Experiment Videos

Last Updated: Jun 19, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Area of Science:

  • Bioinformatics
  • Genomics
  • Cancer Research

Background:

  • High-throughput gene expression profiling is crucial for characterizing tumor biopsies in clinical trials.
  • Machine learning (ML) applied to gene expression data aims to enhance tumor diagnosis, prognosis, and treatment response prediction.
  • Overfitting is a challenge in ML for cancer studies due to limited patient numbers in single trials.

Purpose of the Study:

  • To systematically investigate whether merging gene expression data from multiple breast cancer studies improves survival prediction.
  • To assess if the benefits of increased sample size outweigh the drawbacks of data heterogeneity in ML models.
  • To evaluate the added value of merged datasets using Cox regression for survival prediction.

Main Methods:

  • Utilized time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance metrics.
  • Compared survival prediction performance between individual and merged gene expression datasets.
  • Employed Cox regression analysis to measure the impact of data merging on predictive accuracy.

Main Results:

  • Overall, no significant improvement or deterioration in survival prediction was observed when comparing merged datasets to individual ones.
  • A few potent prognostic genes were excluded from merged datasets due to unavailability across different microarray platforms.
  • The highest predictive performance was unexpectedly achieved using a single-gene predictor based on CYB5D1 expression.

Conclusions:

  • Data merging did not negatively impact survival prediction performance despite variations in microarray platforms, patient cohorts, and disease characteristics.
  • Predictors developed from merged datasets demonstrated enhanced robustness, consistency, and reproducibility across platforms.
  • Merging datasets aids in understanding individual study biases and can reveal significant survival factors, such as CYB5D1.