Understanding how transcriptional and post-transcriptional regulatory mechanisms are altered in human diseases is essential for uncovering disease etiology and identifying novel therapeutic targets. This session explores emerging computational strategies to study gene expression dysregulation across multiple regulatory layers affecting gene expression. The scope of the session spans a broad range of approaches, from statistical and machine-learning models to network-based and integrative multi-omics frameworks. Emphasis will be placed on methods that leverage high-throughput data such as RNA sequencing, single-cell and spatial transcriptomics, epigenomic profiles, and RNA–protein interaction data to dissect transcriptional and post-transcriptional regulatory alterations in complex diseases. Contributions addressing methodological advances, benchmarking strategies, and applications to specific disease contexts—such as cancer, neurological disorders, and immune-related diseases—are particularly encouraged. By bringing together computational scientists and biomedical researchers, this session aims to foster cross-disciplinary dialogue and promote the translation of computational insights into biological and clinical understanding. The expected impact is to advance our ability to systematically characterize transcriptional and post-transcriptional dysregulation, improve disease stratification, and support the discovery of biomarkers and regulatory mechanisms with potential diagnostic and therapeutic relevance.