THItoGene: a deep learning method for predicting spatial transcriptomics from histological images.

ABSTRACT: Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior per

SUBMITTER: Jia Y 

PROVIDER: S-EPMC10749789 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature