Enhancing Predictive Power in Personalized Breast Cancer Treatment through the Integration of Multi-Omics Data and Clinical Information with Deep Learning Models

The paper explores the complexities of personalized breast cancer treatment, by integration of multi-omics data, clinical data, and advanced computational tools. The heterogeneous nature of breast cancer causes challenges to conventional therapies, requiring the need for tailored approaches. The literature review highlights the relevance of predictive analytics in identifying detailed tumor patterns and enhancing personalized treatment efficacy. Integrating multi-omics data and clinical information data with utilising deep learning improves diagnostic precision and prognosis accuracy, forming a robust framework for predicting disease-related genes and influencing treatment decisions. Despite challenges with predicting recurrence risk and comprehending tumor complexity, the incorporation of multi-omics data and clinical information with modern computer techniques shows potential.