Integrating machine learning with OMICs data for early detection in breast cancer

Breast cancer is one of the most common cancers that significantly affects a large population of women, emphasizing its importance in early detection for effective treatments. The advancement in technologies, especially in machine learning and its integration with multi-omics data, such as genomics, transcriptomics, proteomics, metabolomics, and imaging, is not only revolutionizing the diagnosis and prognosis of breast cancer at its early stages but also providing a door for personalized treatment plans to improve patient outcomes. However, achieving truly personalized treatment requires integration of causal inference methods into machine learning frameworks, as correlational models alone may not ensure effective or safe decision-making. The current study revisits the progress made in this research area, providing a comprehensive insight into the challenges of breast cancer early detection, machine learning (ML) in cancer detection, ML-Omics integration, clinical applications, case studies, and future directions and innovations.