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Related Experiment Video

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Development of Symbolic Expressions Ensemble for Breast Cancer Type Classification Using Genetic Programming Symbolic

Nikola Anđelić1, Sandi Baressi Šegota1

  • 1Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.

Cancers
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a genetic programming symbolic classifier (GPSC) to accurately classify breast cancer subtypes using gene expression data. The method achieves high accuracy, aiding in personalized treatment strategies for breast cancer patients.

Keywords:
5-fold cross validationbreast cancergenetic programming symbolic classifierrandom hyperparameter value search

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning in Oncology

Background:

  • Accurate breast cancer sub-typing is crucial for effective treatment selection.
  • Classifying breast cancer using gene expression is challenging due to high dimensionality and data imbalance.
  • Existing methods struggle with large gene expression datasets and limited samples.

Purpose of the Study:

  • To develop a highly accurate breast cancer sub-type classification method using gene expression data.
  • To apply genetic programming symbolic classifier (GPSC) for extracting interpretable symbolic expressions (SEs).
  • To address challenges of high dimensionality, small sample size, and class imbalance in breast cancer datasets.

Main Methods:

  • Principal Component Analysis (PCA) for dimensionality reduction.
  • Oversampling techniques to handle class imbalance and small sample size.
  • Genetic Programming Symbolic Classifier (GPSC) with Random Hyperparameter Value Search (RHVS) and 5-fold cross-validation (5CV).

Main Results:

  • Achieved a peak classification accuracy of 0.992 using GPSC on oversampled datasets.
  • Symbolic expressions (SEs) were generated for breast cancer sub-type classification.
  • Combining SEs with a decision tree classifier further improved accuracy to 0.994.

Conclusions:

  • GPSC is an effective tool for accurate breast cancer sub-type classification from gene expression data.
  • The developed method successfully overcomes common challenges in genomic data analysis.
  • This approach holds promise for improving diagnostic accuracy and guiding personalized cancer treatments.