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Biomarker Discovery Based on Hybrid Optimization Algorithm and Artificial Neural Networks on Microarray Data for

Niloofar Yousefi Moteghaed1, Keivan Maghooli1, Shiva Pirhadi1

  • 1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Journal of Medical Signals and Sensors
|June 30, 2015
PubMed
Summary

This study introduces a hybrid optimization algorithm for identifying cancer biomarkers from gene expression data. The method effectively classifies cancer types, improving diagnostic accuracy and treatment strategies.

Keywords:
Artificial neural networkcancer classificationgene expressiongenetic algorithmparticle swarm optimization algorithm

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput gene profiling using microarrays enables simultaneous monitoring of thousands of gene expressions.
  • Analyzing gene expression changes aids in efficient diagnosis, tumor classification, and effective cancer treatment.

Purpose of the Study:

  • To identify genes capable of correctly classifying cancer types using hybrid optimization algorithms.
  • To improve cancer classification accuracy by selecting informative gene subsets and optimal classifier parameters.

Main Methods:

  • A hybrid particle swarm optimization and genetic algorithm was employed for gene selection.
  • Artificial Neural Network (ANN) was utilized as the classifier.
  • 10-fold cross-validation and decision tree algorithms were used for accuracy assessment and biological interpretation.

Main Results:

  • The proposed method effectively reduced data dimensionality and identified informative gene subsets.
  • Improved classification accuracy was achieved with optimized classifier parameters across multiple datasets.
  • The approach demonstrated proficiency in categorizing cancers using blinded samples.

Conclusions:

  • The developed hybrid optimization approach enhances cancer classification accuracy.
  • The method successfully identifies key biomarkers for improved cancer diagnosis and subtyping.
  • This technique offers a robust tool for analyzing gene expression data in oncology.