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Survival Tree01:19

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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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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

Tree Pruner: An efficient tool for selecting data from a biased genetic database.

Mohan Krishnamoorthy1, Pragneshkumar Patel, Mira Dimitrijevic

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.

BMC Bioinformatics
|February 11, 2011
PubMed
Summary
This summary is machine-generated.

Selecting high-quality genetic data is challenging due to database biases. Tree Pruner offers a manual, visual tool to curate datasets with desired evolutionary properties, ensuring better downstream analyses.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genetic databases often exhibit representation biases, complicating the selection of data with specific evolutionary or genotypic properties.
  • Epidemiological variable-based selection may not yield desired evolutionary characteristics.
  • Automated data selection methods for influenza genetic data involve a trade-off between speed/simplicity and control over dataset quality.

Purpose of the Study:

  • To develop a tool for selecting genetic datasets with specific evolutionary properties from large, biased databases.
  • To provide researchers with enhanced control over dataset quality and content for genetic analyses.

Main Methods:

  • Developed Tree Pruner, an interactive tool utilizing phylogenetic trees for dataset editing.
  • Implemented dynamic tree visualization with color-coding to reflect user actions.
  • Integrated Tree Pruner with the Influenza Research Database (IRD) for data management and analysis.

Main Results:

  • Tree Pruner enables users to interactively refine genetic datasets based on phylogenetic structure.
  • The tool dynamically updates tree visualizations to guide the selection process.
  • Pruned datasets can be stored and managed within the IRD for further analysis or refinement.

Conclusions:

  • Tree Pruner is an effective manual tool for curating high-quality genetic datasets from biased sources.
  • It provides superior control over dataset content and quality compared to automated methods.
  • The visual and interactive nature of Tree Pruner aids researchers in achieving desired evolutionary properties in their selected data.