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Improved NSGA-II algorithms for multi-objective biomarker discovery.

Luca Cattelani1, Vittorio Fortino1

  • 1School of Medicine, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.

Bioinformatics (Oxford, England)
|September 20, 2022
PubMed
Summary
This summary is machine-generated.

Improved Non-dominated Sorting Genetic Algorithm II (NSGA2) methods enhance biomarker discovery by achieving high accuracy with fewer features. These algorithms offer a promising alternative for translational research, outperforming existing methods in breast cancer gene expression analysis.

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Area of Science:

  • Biomedical Informatics
  • Translational Research
  • Computational Biology

Background:

  • Biomarker discovery using omics technologies is crucial for translational research.
  • Basic data mining algorithms often yield false positives in biomarker discovery.
  • Non-dominated Sorting Genetic Algorithm II (NSGA2) is effective but underexplored on large datasets, sometimes showing shallow feature space exploration.

Purpose of the Study:

  • To develop improved NSGA2 algorithms for identifying biomarker subsets with optimized accuracy-feature number trade-offs.
  • To evaluate these novel algorithms on breast cancer gene expression data.
  • To compare their performance against existing NSGA2 and LASSO methods.

Main Methods:

  • Proposed two enhanced NSGA2 algorithms for multi-objective optimization.
  • Applied algorithms to gene expression data from breast cancer patients.
  • Benchmarked results using internal and external validation sets against NSGA2 and LASSO.

Main Results:

  • The proposed algorithms yielded a superior approximation of the accuracy-feature number trade-off.
  • Achieved higher validation and test accuracies compared to NSGA2 and LASSO.
  • Identified biomarker sets with >80% prediction accuracy using <10 features.

Conclusions:

  • Enhanced NSGA2 algorithms provide a robust alternative for biomarker discovery.
  • These methods offer a significant improvement over standard NSGA2 and LASSO for large-scale datasets.
  • The developed algorithms facilitate the discovery of highly accurate and parsimonious biomarker panels for breast cancer.