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Related Concept Videos

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Related Experiment Video

Updated: May 7, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for

Luca Cattelani1, Vittorio Fortino1

  • 1School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.

Briefings in Bioinformatics
|December 31, 2024
PubMed
Summary

We developed a new algorithm, DOSA-MO, to improve biomarker panel selection from omics data. It reduces overestimation errors during optimization, leading to more accurate cancer subtype and survival predictions.

Keywords:
biomarker discoverygenetic algorithmsmodel selectionmulti-objectiveomicsoverestimation adjustment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Genomics

Background:

  • Biomarker panel selection from omics data is challenging due to high dimensionality and limited samples.
  • Wrapper feature selection methods, like genetic algorithms, are used with machine learning for biomarker discovery.
  • Existing methods often overestimate model performance, especially in multi-objective optimization.

Purpose of the Study:

  • To address performance overestimation in multi-objective biomarker selection during optimization.
  • To introduce a novel algorithm, Dual-stage Optimizer for Systematic overestimation Adjustment in Multi-Objective problems (DOSA-MO).
  • To improve the selection of biomarker panels for enhanced predictive accuracy.

Main Methods:

  • Developed DOSA-MO, a multi-objective optimization wrapper algorithm.
  • DOSA-MO learns to predict and adjust for performance overestimation during optimization.
  • Evaluated DOSA-MO by comparing it with a state-of-the-art genetic algorithm.

Main Results:

  • DOSA-MO significantly improves the performance of genetic algorithms on external datasets.
  • The algorithm enhances the accuracy of cancer subtype classification.
  • Improved prediction of patient overall survival was observed.

Conclusions:

  • DOSA-MO effectively reduces performance overestimation in multi-objective biomarker selection.
  • The proposed method enhances the reliability and accuracy of biomarker panels identified from omics data.
  • DOSA-MO offers a valuable advancement for machine learning applications in cancer research.