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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
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Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression.

Abdulrhman M Alshareef1, Raed Alsini1, Mohammed Alsieni2

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Journal of Healthcare Engineering
|March 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-driven model for prostate cancer detection using gene expression data. The approach enhances early cancer classification by optimizing feature selection and deep learning for improved accuracy.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Prostate cancer is a leading global cause of mortality, necessitating improved early detection and classification methods.
  • Existing statistical and machine learning approaches for prostate cancer detection face challenges with high-dimensional data and limited training samples.
  • Metaheuristic algorithms offer potential solutions for dimensionality reduction and enhancing artificial intelligence (AI) detection rates.

Purpose of the Study:

  • To develop an AI-based feature selection with deep learning model for accurate prostate cancer detection (AIFSDL-PCD) using microarray gene expression data.
  • To address the challenges of high dimensionality and limited samples in prostate cancer classification.
  • To improve the detection rate and reduce computational complexity in AI-driven cancer diagnostics.

Main Methods:

  • Data preprocessing was performed to enhance the quality of microarray gene expression data.
  • A novel Chaotic Invasive Weed Optimization (CIWO) based feature selection (FS) technique was employed to identify an optimal subset of features.
  • A Deep Neural Network (DNN) model, optimized with RMSprop, was utilized for prostate cancer classification.

Main Results:

  • The AIFSDL-PCD technique demonstrated superior performance compared to existing methods in distinct evaluation measures.
  • The CIWO-based feature selection effectively reduced computational complexity.
  • The deep learning model achieved improved classification accuracy for prostate cancer detection.

Conclusions:

  • The developed AIFSDL-PCD approach offers a promising AI-driven solution for enhanced prostate cancer detection.
  • Optimized feature selection using CIWO significantly contributes to improved classification accuracy and efficiency.
  • This study highlights the potential of integrating advanced AI techniques with gene expression data for more effective cancer diagnostics.