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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Information graphs for binary predictors.

G Hughes1, N McRoberts1, F J Burnett1

  • 1First and third authors: Crop and Soil Systems Research Group, SRUC, The King's Buildings, West Mains Road, Edinburgh EH9 3JG, UK; second author: Plant Pathology Department, University of California, Davis, CA 95616-8751, USA.

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Summary

This study explores binary predictors for crop disease risk assessment. Information theory concepts like entropy and mutual information are used to interpret diagnostic probabilities, enhancing decision-making in crop protection.

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Area of Science:

  • Agricultural Science
  • Information Theory
  • Biostatistics

Background:

  • Binary predictors are crucial for crop protection decision-making.
  • Bayesian updating of crop disease probabilities relies on risk factor evidence.
  • Receiver operating characteristic curves aid in interpreting diagnostic probabilities.

Purpose of the Study:

  • To analyze binary predictors through the lens of diagnostic information.
  • To provide diagrammatic interpretations of key information-theoretic measures.
  • To illustrate the relationship between diagnostic information and probabilities.

Main Methods:

  • Introduction to entropy and expected mutual information.
  • Application of information theory to an example dataset.
  • Diagrammatic interpretation using information graphs.

Main Results:

  • Demonstration of expected mutual information, relative entropy, and information inaccuracy.
  • Visualization of information updating and specific information.
  • Illustration of correspondences between diagnostic information and probabilities.

Conclusions:

  • Binary predictors can be effectively analyzed using information theory.
  • Information graphs offer valuable insights into diagnostic probabilities for crop protection.
  • This approach enhances the understanding of risk factors in disease prediction.