فهرست نشریات علمی دسترسی آزاد ایران

Gene expression signatures‎ reflect the response of cell tissues to diseases‎, ‎genetic disorders‎, ‎and drug treatments‎, ‎ containing hidden patterns that can provide valuable insights for biological research and cancer diagnostics‎. ‎This study‎proposes a hybrid deep learning approach combining convolutional neural networks (CNNs) and support vector machines (SVMs) to classify cancer types using unstructured gene expression data‎. ‎ We applied three hybrid CNN-SVM models to a dataset of 10,340 samples spanning 33 cancer types from the Cancer Genome Atlas‎‎. ‎The CNN component extracted latent features from the gene expression data‎, ‎while the SVM replaced the softmax layer to enhance classification robustness‎. ‎ Among the proposed models‎, ‎the Hybrid-CNN-SVM model achieved superior performance‎, ‎demonstrating excellent prediction accuracy and outperforming other models‎. ‎This study highlights the potential of hybrid deep learning frameworks for cancer type prediction and underscores their applicability to high-dimensional genomic datasets‎.