AI-Driven Transcriptomics: Advancing Gene Expression Analysis and Precision Medicine

Transcriptomics, the comprehensive study of RNA transcripts, has transformed our understanding of cellular functions and disease mechanisms by capturing real-time gene expression profiles. However, the sheer scale and complexity of RNA sequencing (RNA-Seq) data-particularly in single-cell experiments-pose significant computational and interpretive challenges. Artificial intelligence (AI), which encompasses machine learning (ML) and deep learning (DL), has emerged as a key driver in handling these challenges, offering data-driven solutions that enhance expression quantification, functional annotation, and clinical translation. By integrating multi-omics datasets, clinical records, and large-scale transcriptomic outputs, AI-powered approaches can detect subtle expression patterns, identify novel biomarkers, and inform patient-specific therapeutic strategies. This article highlights current AI applications in transcriptomics, discusses technical and ethical considerations, and outlines future directions for applying AI in precision medicine. Ultimately, AI has the potential to streamline transcriptomic analysis, refine disease characterization, and improve patient outcomes through more targeted interventions.