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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Optimizing hierarchical tree dissection parameters using historic epidemiologic data as 'ground truth'.

David Jacobson1,2, Joel Barratt1

  • 1Parasitic Diseases Branch, Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.

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PubMed
Summary

Optimizing the stringency parameter (s) in hierarchical clustering improves pathogen outbreak investigations. This method enhances the accuracy of genetic partitions, aiding in the identification of common food sources for Cyclospora spp. outbreaks.

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Area of Science:

  • Genomics
  • Epidemiology
  • Computational Biology

Background:

  • Hierarchical clustering of pathogen genotypes is crucial for outbreak investigations.
  • Dissecting genetic trees requires meaningful partitions for epidemiologists.
  • Statistical tree dissection methods often rely on user-defined parameters that impact results.

Purpose of the Study:

  • To demonstrate an optimization method for tree dissection parameters to maximize accuracy.
  • To identify the optimal stringency parameter (s) for clustering Cyclospora spp. genotypes.
  • To improve the reliability of genetic partitions in outbreak investigations.

Main Methods:

  • Hierarchical clustering of 1,873 Cyclospora spp. genotypes.
  • Dissection of the resulting phylogenetic tree using a statistical method with varying stringency parameter (s) values (94%–99.5%).
  • Evaluation of partitioning accuracy based on conformity to known epidemiologic groupings.

Main Results:

  • Optimized s-values of 96.5% and 96.75% yielded the highest partitioning accuracy (>99.9%) for Cyclospora spp.
  • The optimized parameter maximized the accuracy of genetic partitions, irrespective of the tree dissection method.
  • Accurate genetic partitions can identify isolates likely originating from a common food source.

Conclusions:

  • The optimization approach enhances the accuracy of genetic partitions in pathogen outbreak investigations.
  • Optimized stringency parameter values provide robust groupings for epidemiological analysis.
  • This method offers a generalizable approach for optimizing parameters in hierarchical tree dissection.