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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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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
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Interpretation of Confidence Intervals

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Uncertainty: Confidence Intervals00:54

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Using minimum bootstrap support for splits to construct confidence regions for trees.

Edward Susko1

  • 1Genome Atlantic, Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada. susko@mathstat.dal.ca.

Evolutionary Bioinformatics Online
|May 21, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm to identify all phylogenetic trees with a minimum bootstrap support threshold. This method provides a more comprehensive understanding of phylogenetic uncertainty and confidence regions for tree topologies.

Keywords:
bootstrap supportconfidence regionsphylogenysplitsstatistical tests

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Bioinformatics

Background:

  • Phylogenetic studies often rely on bootstrap support values to indicate the reliability of estimated splits (branches) in evolutionary trees.
  • Low bootstrap support can be ambiguous, failing to specify which taxa might be misplaced or how many could be affected.

Purpose of the Study:

  • To develop an algorithm that identifies all phylogenetic trees meeting a specified minimum bootstrap support threshold for their splits.
  • To provide a more detailed assessment of phylogenetic uncertainty beyond single tree topologies.

Main Methods:

  • An algorithm was developed to find the set of all trees with a minimum bootstrap support for their splits above a given value.
  • The output is a ranked list of trees, ordered by their minimum bootstrap support.
  • A double bootstrap approach was used to determine a cutoff for an approximate 95% confidence region of topologies.

Main Results:

  • The algorithm generates a ranked list of trees, offering supplementary information on the reasons behind low bootstrap support.
  • The set of all topologies above a certain bootstrap threshold quantifies low bootstrap support, defining a confidence region.
  • A double bootstrap method allows for the selection of a cutoff to establish an approximate 95% confidence region for phylogenetic topologies.

Conclusions:

  • The presented method offers a more nuanced interpretation of bootstrap support in phylogenetics.
  • It provides a quantifiable confidence region of topologies, enhancing the understanding of phylogenetic uncertainty.
  • The approach is broadly applicable across various phylogenetic estimation methods.