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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: Jan 9, 2026

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
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Tree-based SIMCA for dealing with heterogeneous and sparse data.

Robert van Vorstenbosch1, Frederik-Jan van Schooten2, Zlatan Mujagic3

  • 1Department of Pharmacology and Toxicology, Maastricht University, the Netherlands; NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University, the Netherlands; Institute of Risk Assessment Sciences, Population Health Sciences, Utrecht University, the Netherlands.

Analytica Chimica Acta
|December 6, 2025
PubMed
Summary
This summary is machine-generated.

Tree-based methods improve One Class Classification (OCC) for omics data, addressing limitations of traditional SIMCA. Unsupervised Random Forest-SIMCA (URF-SIMCA) offers superior performance and interpretability for omics datasets.

Keywords:
Isolation forestNon-linearOmicsPseudosamplingSoft independent modelling of class analogyURF-SIMCAUnsupervised random forest

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Area of Science:

  • Cheminformatics and Bioinformatics
  • Machine Learning in Omics Data Analysis

Background:

  • One Class Classification (OCC) methods like SIMCA face challenges with omics data, including high dimensionality, sparsity, and non-linearities.
  • These issues often lead to inflated decision boundaries and false positives in identifying target classes.
  • Tree-based techniques offer inherent robustness to these data characteristics.

Purpose of the Study:

  • To explore and evaluate tree-based variants of SIMCA for omics data analysis.
  • To compare the performance of these novel methods against traditional OCC strategies.
  • To enhance the interpretability of omics data models.

Main Methods:

  • Developed non-linear SIMCA variants using sample proximities from Unsupervised Random Forest (URF-SIMCA) and Isolation Forest (IF-SIMCA).
  • Compared URF-SIMCA and IF-SIMCA against traditional SIMCA, one-class SVM, and Isolation Forest.
  • Utilized five clinical omics datasets and the wine dataset for empirical validation.

Main Results:

  • URF-SIMCA demonstrated superior performance across the tested omics datasets.
  • Pseudo-sampling principles enabled feature interpretation for class separation.
  • Feature trajectories in score and orthogonal distance spaces enhanced model interpretability.

Conclusions:

  • URF-SIMCA provides an accessible extension to SIMCA, effectively reducing target class variance for improved separation.
  • While feature interpretation is slightly reduced, the pseudo-sampling principle offers a viable solution.
  • The method enhances modeling performance in omics data analysis.