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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: May 11, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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High performance data integration for large-scale analyses of incomplete Omic profiles using Batch-Effect Reduction

Yannis Schumann1, Simon Schlumbohm2, Julia E Neumann3,4

  • 1Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany. yannis.schumann@desy.de.

Nature Communications
|August 2, 2025
PubMed
Summary

Batch-effect Reduction Trees (BERT) efficiently integrate incomplete omic data, overcoming missing values and biases. This high-performance method enhances quantitative comparison across diverse, large-scale datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • High-throughput omic data frequently contains missing values and batch-effects, impeding quantitative analysis and dataset integration.
  • Existing methods struggle with incomplete omic profiles, limiting the scope of integrated analyses.

Purpose of the Study:

  • Introduce Batch-effect Reduction Trees (BERT), a novel high-performance method for integrating incomplete omic data.
  • Address limitations of current data integration techniques for omics and other datatypes.

Main Methods:

  • Developed BERT, a method designed for efficient data integration of omic profiles with missing values.
  • Characterized BERT on large-scale integration tasks involving up to 5000 datasets across various omic types (proteomics, transcriptomics, metabolomics) and datatypes (clinical data).
  • Leveraged multi-core and distributed-memory systems for computational efficiency.

Main Results:

  • BERT retains significantly more numeric values compared to existing methods.
  • Achieved up to an 11-fold runtime improvement by utilizing parallel processing capabilities.
  • Improved data integration quality, evidenced by up to a 2-fold increase in average silhouette width, by considering covariates and reference measurements.

Conclusions:

  • BERT offers a robust and scalable solution for integrating incomplete omic datasets, enhancing quantitative comparability.
  • The method demonstrates broad applicability across diverse omic types and other data modalities.
  • BERT significantly outperforms existing methods in data retention, runtime, and integration quality.