Introduction. The purpose of this study is to use deep learning and machine learning to learn and classify patients with cutaneous melanoma with different prognoses and to explore the application value of deep learning in the prognosis of cutaneous melanoma patients. Methods. In deep learning, VGG-19 is selected as the network architecture and learning model for learning and classification. In machine learning, deep features are extracted through the VGG-19 network architecture, and the support vector machine (SVM) model is selected for learning and classification. Compare and explore the application value of deep learning and machine learning in predicting the prognosis of patients with cutaneous melanoma. Result. According to receiver operating characteristic (ROC) curves and area under the curve (AUC), the average accuracy of deep learning is higher than that of machine learning, and even the lowest accuracy is better than that of machine learning. Conclusion. As the number of learning increases, the accuracy of machine learning and deep learning will increase, but in the same number of cutaneous melanoma patient pathology maps, the accuracy of deep learning will be higher. This study provides new ideas and theories for computational pathology in predicting the prognosis of patients with cutaneous melanoma.

Objective The cell cycle-related proteins cyclin B1 (CCNB1) and cyclin B2 (CCNB2) are potentially involved in the underlying mechanisms of psoriasis. The present study aimed to explore this possibility using bioinformatics approaches. Methods CCNB1 and CCNB2 protein levels were evaluated in 14 psoriasis patients and five healthy controls by enzyme-linked immunosorbent assays, and their mRNA levels were evaluated using data from four publicly available datasets (GSE53552, GSE41664, GSE14905, and GSE13355). Comparison of high- and low-expressing groups were performed to reveal CCNB1- and CCNB2-related differentially expressed genes, which were then assessed based on gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Correlation analyses between CCNB1 and CCNB2 levels and immune infiltration, as well as typical targets of psoriasis, were also performed. Results Overall, 12 CCNB1 and CCNB2 common immune-related targets potentially involved in psoriasis were identified. These could regulate the cell cycle of through multiple pathways. In addition, CCNB1 and CCNB2 were found to potentially support the release of key molecular targets of psoriasis through the regulation of mast cell activation and macrophage polarization. Conclusions These findings suggest that CCNB1 and CCNB2 may represent valuable molecular biomarkers of psoriasis, contributing to its onset and progression.

No abstract