Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques.

ABSTRACT: Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen-Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematic

SUBMITTER: Venkatesan VK 

PROVIDER: S-EPMC10670706 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature