Nagwa Badr

Abstract The lack of validated stage-specific biomarkers hampers the understanding of Alzheimer’s disease (AD) progression and clinical translation. Current transcriptomic methods often produce unstable results with limited stage discrimination. We aimed to develop an explainable machine learning pipeline to identify robust, interpretable gene signatures linked to distinct AD neuropathological stages.

Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References , , , and . 2021. “Effect of High-Tone Power Therapy on Neurological Symptoms and Functional Outcomes in Patients With Diabetic Neuropathy.” Egyptian Journal of Basic and Applied Sciences 36, no. 1: 10–21. https://doi.org/10.21608/ejas.2021.152343. , , , , , and . 2021.

Abstract Skin cancer is the abnormal growth of skin cells, most often developing on skin exposed to the sun. It is among the most fatal forms of cancer, making its early detection and therapy crucial. In addition to conventional techniques, deep learning methods are increasingly utilized for accurate identification and classification. This study proposes a convolutional neural network (CNN) model using transfer learning to detect and classify multiple types of skin cancer.