Revolutionizing Breast Cancer Subtyping: A Multi-Omic and Pan-Cancer Deep Learning Approach

Deep learning methods are increasingly used in biomedical research, including for cancer subtyping. This study explores deep learning-based breast cancer subtyping using pan-cancer and multi-omic data. It proposes a hybrid model combining Bidirectional Long Short-Term Memory (BiLSTM) networks and XGBoost for temporal dependency and ensemble learning. Convolutional Neural Networks (CNNs) are also used to detect spatial patterns in multi-omic data. By combining these methods, the study aims to develop a more accurate breast cancer subtyping approach. The pan-cancer approach ensures the model's applicability across different cancer types. This research is expected to improve precision medicine and support more effective therapeutic strategies.