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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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PROPER: Performance visualization for optimizing and comparing ranking classifiers in MATLAB.

Samad Jahandideh1, Fatemeh Sharifi2, Lukasz Jaroszewski1

  • 1Bioinformatics and Systems Biology Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92307 USA.

Source Code for Biology and Medicine
|December 5, 2015
PubMed
Summary
This summary is machine-generated.

Researchers developed PROPER, a MATLAB package for visually evaluating ranking classifiers. This tool aids in biological big data mining by optimizing and comparing classifier performance using diverse curves.

Keywords:
Big dataPredictive modelingScoring classifierStructural genomics

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Data mining

Background:

  • High-throughput technologies generate large biomedical datasets, posing challenges for predictive modeling.
  • Development of advanced algorithms, tools, and software is crucial for analyzing this big data.

Purpose of the Study:

  • To develop a novel package for the visual evaluation of ranking classifiers.
  • To facilitate big data mining in biological studies using predictive modeling.

Main Methods:

  • Developed PROPER, a software package within the MATLAB environment.
  • Implemented visual evaluation methods for ranking classifiers.

Main Results:

  • PROPER provides a tool for the visual evaluation of ranking classifiers.
  • The package supports biological big data mining studies.

Conclusions:

  • PROPER is an efficient tool for optimizing and comparing ranking classifiers.
  • Offers over 20 different two- and three-dimensional performance curves for comprehensive analysis.