AI Approaches for Constructing Digital Transcriptomics

The integration of AI in transcriptomics has revolutionized the analysis of complex biological data. This chapter highlights AI's role in digital transcriptomics, especially in spatial transcriptomics, pathology, and toxicity studies. AI techniques like ML and DL tackle high-dimensional data through preprocessing, feature extraction, and modeling. Tools such as HE2RNA predict RNA-Seq data from histology slides, aiding virtual gene expression mapping and tumor analysis. GANs, like TransTox, translate organ-specific profiles to support drug safety. In cancer immunotherapy, AI helps detect immune signatures and tumor heterogeneity. AI also enables multi-omics integration, improving signal clarity and accelerating precision medicine.