Pro-os: a Machine Learning-based Prognostic Prediction Model for Osteosarcoma
Year: 2025
Type: article
Abstract: The research aimed to develop an effective prognostic model for osteosarcoma. This study included 72 osteosarcoma cases from the Target OS database and 87 cases from the Shanghai General Hospital (SGH) Cohort. DNA methylation data and RNA-seq data collected from the Target OS database and the SGH Cohort were processed using synthetic minority over-sampling technique (SMOTE), Tomek links, convolutional neural network (CNN), and Random Forest models. Based on the prognosis of the patients, we divided the cases into a death/survival group and a recurrence/nonrecurrence group. A total of 10 times of model training and testing were performed separately, using the test results as model evaluation parameters. We evaluated the model performance by comprehensively assessing its precision, recall, accuracy, and F1 score. The results of this study offer a robust methodology for determining the prognosis and characteristics of osteosarcoma patients, applicable even to smaller data sets. This approach provides novel insights into the molecular data analysis of rare diseases.
Authors: KE ZENG, XIYU YANG, HAORAN MU, YINGQI HUA, ZHENGDONG CAI
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4407239415