SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping.

ABSTRACT: Optimizing genomic-based molecular subtyping is key to promote personalized medicine. While neural networks face setbacks regarding tabular data modeling, deep learning has undergone groundbreaking advances across multiple domains, catalyzing further breakthroughs across AI applications. New neural network architectures exhibit enhanced performance, efficiency, and robustness, which could benefit the genomic use-case. In this study we introduce SubNExT, an optimized shallow CNN with a ConvNeXt backbone using t-SNE and DeepInsight 2D-converted gene expression for breast cancer subtyping. It was compared with other modelization strategies for gene expression data, by optimizing a Transformer, an MLP and XGBoost for unconverted values, a 1D CNN (NeXt-TDNN) for ordered values, and a ViT as an

SUBMITTER: Paygambar K 

PROVIDER: S-EPMC12834934 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature