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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis.

Dongfang Jia1, Cheng Chen1, Chen Chen1

  • 1College of Information Science and Engineering, Xinjiang University, Urumqi, China.

Frontiers in Genetics
|June 3, 2021
PubMed
Summary

This study introduces a novel, radiation-free method for breast cancer (BC) diagnosis using gene expression profiles. The artificial neural network (ANN) model achieved high accuracy, offering a rapid and sensitive alternative to traditional methods.

Keywords:
ANNPPISVMWGCNAbreast cancer

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Current breast cancer (BC) diagnostic technologies like mammography and MRI have limitations.
  • Understanding BC molecular mechanisms is crucial for improved diagnostics.

Purpose of the Study:

  • To develop a rapid, sensitive, and radiation-free BC diagnostic method.
  • To classify BC and normal samples using gene expression profiles from TCGA and GEO databases.

Main Methods:

  • Weighted gene co-expression network analysis (WGCNA) and differential expression analysis (DEA) identified key genes.
  • Protein-protein interaction (PPI) network analysis screened 23 hub genes.
  • Machine learning models including ANN, SVM, DT, BN, CNN-LeNet, and CNN-AlexNet were employed for classification.

Main Results:

  • The artificial neural network (ANN) model demonstrated superior performance in classifying BC samples.
  • The ANN model achieved an average accuracy of 97.36%, with a sensitivity of 98.32% and specificity of 89.59%.

Conclusions:

  • The proposed gene expression profiling method effectively classifies breast cancer samples.
  • This approach offers a promising, radiation-free alternative for early cancer diagnosis.