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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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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Correspondence: Accuracy Is Not Enough: Stability-Aware Feature Selection for Reproducible Biomarker Discovery.

Yoshiyasu Takefuji1

  • 1Faculty of Data Science, Musashino University, Tokyo, Japan.

Allergy
|September 23, 2025
PubMed
Summary

Random forest models offer high accuracy but unstable feature importance. Stability-aware methods like FA, HVGS, and Spearman correlation provide more reliable feature selection for reproducible biomarker discovery.

Area of Science:

  • Bioinformatics
  • Machine Learning
  • Computational Biology

Background:

  • Random forest (RF) models are widely used for high predictive accuracy.
  • However, RF's model-specific feature importances can be unstable and misleading, hindering reproducible biomarker discovery.
Keywords:
biomarker discoveryfeature selectionrandom forestreproducibilitystability

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