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Related Experiment Video

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Cancer classification from microarray data for genomic disorder research using optimal discriminant independent

Tram Thi Huyen Nguyen1, Pol Van Nguyen2, Quang Vinh Tran2

  • 1Department of Pharmacy, Ear - Nose - Throat Hospital in Ho Chi Minh city, Ho Chi Minh City, Vietnam.

International Journal for Numerical Methods in Biomedical Engineering
|May 27, 2020
PubMed
Summary

This study introduces a hybrid feature selection method for classifying genomic disorders from microarray data, reducing costs and improving efficiency. The novel approach enhances accuracy in identifying complex diseases from large datasets.

Keywords:
datasetgene selectionkernel extreme learning machinemicroarraymodified firefly based discriminant independent component analysis

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic disorder investigation and classification from microarray data is complex and costly.
  • High dimensionality of microarray datasets presents significant challenges in biomedical research.
  • Efficient feature selection and extraction are crucial for accurate disease classification.

Purpose of the Study:

  • To develop a hybrid feature selection and extraction method for improved microarray data classification.
  • To reduce the time and cost associated with genomic disorder analysis.
  • To enhance the accuracy of classifying complex diseases from high-dimensional genomic data.

Main Methods:

  • A hybrid feature selection approach combining t-test, Fisher ratio, and Bayesian logistic regression.
  • Gene feature selection using a best hybrid rank method.
  • Feature extraction via modified firefly optimization-based discriminant independent component analysis (MF-DICA).
  • Classification of gene features using kernel extreme learning machine.

Main Results:

  • The MF-DICA method demonstrated improved search efficiency for feature extraction.
  • The proposed hybrid method effectively reduced time costs in microarray data analysis.
  • Accurate classification of gene features was achieved across six diverse datasets (Leukemia, DLBCL, Lung, Breast, Prostate, Colon).

Conclusions:

  • The developed hybrid feature selection and MF-DICA extraction method is highly suitable for classifying microarray data.
  • The approach offers a more efficient and cost-effective solution for genomic disorder investigation.
  • This method shows significant potential for advancing disease classification in biomedical research.