Transcriptomic profiling technologies, including bulk, single-cell, spatial, and non-coding RNA sequencing, generate complex and heterogeneous datasets that require advanced bioinformatics and computational methods for effective analysis. Beyond their foundational role in biological research, transcriptomic approaches are increasingly being translated into clinically actionable insights, supporting biomarker discovery, disease stratification, prognosis, and therapeutic decision-making. This special session aims to bring together researchers developing and applying computational, statistical, and AI-driven methods for transcriptomics data analysis, with a particular emphasis on the path from methodological innovation to clinical impact. Contributions addressing robustness, interpretability, reproducibility, and integration with clinical and multi-omics data are especially encouraged, in order to bridge the gap between transcriptomics research and real-world clinical applications.