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Updated: May 18, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
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Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Performance reproducibility index for classification.

Mohammadmahdi R Yousefi1, Edward R Dougherty

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

Bioinformatics (Oxford, England)
|September 8, 2012
PubMed
Summary
This summary is machine-generated.

A new reproducibility index helps determine if preliminary classification results from small samples warrant large-scale experiments. Low probability of reproducible error suggests halting further resource allocation for biomarker discovery studies.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Last Updated: May 18, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Biostatistics
  • Computational Biology
  • Machine Learning

Background:

  • Biomarker discovery often relies on preliminary studies with small sample sizes to justify large-scale experiments.
  • Evaluating the efficacy of this approach is crucial for efficient resource allocation in scientific research.

Purpose of the Study:

  • Introduce a probabilistic measure, the reproducibility index, to assess if preliminary classification results are likely to hold in larger samples.
  • Provide a framework to decide whether to proceed with extensive follow-on studies based on initial findings.

Main Methods:

  • Developed a reproducibility index for classification based on error estimates from preliminary studies.
  • Conducted simulation studies using synthetic data with known classification difficulties.
  • Validated results using four real-world datasets and various classification schemes.

Main Results:

  • Empirically calculated reproducibility indices across different models, datasets, and classification methods.
  • Analyzed the impact of reporting and multiple-rule biases on the index.
  • Demonstrated consistency of results between synthetic and real data.

Conclusions:

  • The reproducibility index quantifies the probability of reliable results from small-sample studies.
  • A low probability of reproducible error indicates that large follow-on studies may not be justified.
  • The index aids in making informed decisions about resource allocation in biomarker discovery.