Molecular Subtypes

Molecular subtyping aims to categorize diseases, particularly cancers, into distinct groups based on their underlying molecular characteristics, enabling personalized treatment strategies. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) and other architectures, often in conjunction with multiple instance learning (MIL) to analyze high-dimensional data like gene expression profiles, whole-slide images (WSIs), and even medical imaging (MRI). These methods show promise in improving diagnostic accuracy and potentially streamlining clinical workflows, particularly for cancers like breast and colorectal cancer, where subtype-specific therapies exist. However, challenges remain in addressing biases in training data and ensuring robust performance across diverse patient populations.