Cancer Subtyping

Cancer subtyping aims to classify tumors into distinct groups based on their molecular characteristics, improving diagnosis, treatment selection, and prognosis. Current research heavily utilizes multi-omics data integration, employing machine learning models such as variational Bayesian mixtures, self-organizing maps, and various deep learning architectures (including transformers, convolutional neural networks, and attention mechanisms) to identify subtypes and relevant biomarkers. These advancements are improving the accuracy and efficiency of cancer subtyping, potentially leading to more personalized and effective cancer care. Furthermore, the development of large, annotated datasets like BRACS is crucial for training and validating these sophisticated models.