Enhancing Precision Oncology: Deep Learning Models vs. Classical Machine Learning Models in Multi-Label Breast Cancer Classification

Advancements in single-cell RNA sequencing (scRNA-seq) provide critical insights into cancer heterogeneity, yet analyzing high-dimensional data remains challenging. This study compares GRU with Low-Rank Adaptation (LoRA), Transformer, and XGBoost for multi-label breast cancer classification. Using k-fold cross-validation and paired t-tests, results show GRU-LoRA and Transformer outperform XGBoost in accuracy, precision, recall, and F1-score, particularly for rare cancer subtypes. While XGBoost offers interpretability, deep learning models excel in capturing complex gene interactions. These findings underscore the potential of deep learning in precision oncology, enabling more scalable and accurate diagnostic tools.