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Microarray Analysis for Saccharomyces cerevisiae
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ROC Curves for the Statistical Analysis of Microarray Data.

Ricardo Cao1, Ignacio López-de-Ullibarri2

  • 1Research Group MODES, Department of Mathematics, CITIC and ITMATI, Universidade da Coruña, A Coruña, Spain. rcao@udc.es.

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|May 23, 2019
PubMed
Summary
This summary is machine-generated.

This chapter introduces Receiver Operating Characteristic (ROC) curves for analyzing microarray data. It covers their motivation, existing research, and advanced methods like LASSO and multiple testing correction for marker selection.

Keywords:
AUCFDRFWERLASSOMicroarrayMultiple testingROC curvepAUC

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Area of Science:

  • Bioinformatics
  • Statistical Learning
  • Genomics

Background:

  • Receiver Operating Characteristic (ROC) curves are essential for evaluating binary classifiers.
  • Microarray data analysis presents unique challenges due to high dimensionality.

Purpose of the Study:

  • To provide a comprehensive overview of ROC curve applications in microarray data analysis.
  • To review existing scientific contributions and introduce advanced techniques.

Main Methods:

  • Introduction to the fundamental concepts and motivation behind ROC curves.
  • Review of relevant scientific literature on ROC curves for microarrays.
  • Exploration of specialized methods including LASSO for marker selection and multiple testing correction.

Main Results:

  • ROC curves offer a robust graphical method for assessing diagnostic ability in high-dimensional data.
  • LASSO techniques effectively select and combine relevant markers.
  • Appropriate multiple testing correction is crucial for reliable results.

Conclusions:

  • ROC curve analysis is a valuable tool for microarray data interpretation.
  • Advanced statistical methods enhance the utility of ROC curves in complex genomic studies.
  • This chapter serves as a foundational guide for researchers in the field.