An Integrative Omics-Machine Learning Framework for Predicting Pulmonary Responses to Titanium Dioxide Nanoparticles

Puntos clave

• This research aims to improve the prediction of lung responses to titanium dioxide nanoparticles using a novel omics approach.
• Re-optimized the Transcriptomic Response Index (TRI) for lung data.
• Utilized machine learning techniques to analyze complex transcriptomic data.
• Developed an integrative framework combining omics data with predictive algorithms.
• The optimized TRI serves as a reliable omics endpoint for evaluating pulmonary responses.
• Machine learning effectively predicts responses to titanium dioxide nanoparticles in lung tissues.