The rapid evolution of transcriptome sequencing technologies has driven significant breakthroughs across the life sciences. The advent of single-cell RNA-sequencing (scRNA-seq) has enabled gene expression profiling at single-cell resolution, whereas spatial transcriptomics further contextualizes these transcriptional profiles within preserved tissue morphology. Concurrently, advancements in artificial intelligence have introduced unprecedented opportunities in bioinformatics. As a core component of artificial intelligence, machine learning (ML) substantially outperforms traditional computational methods in deciphering complex, high-dimensional biological data. This review systematically summarizes the significant advantages of integrating ML algorithms into transcriptomic workflows. By leveraging these advanced computational tools, researchers can efficiently extract comprehensive biological insights, elucidate intricate Gene Regulatory Networks, and generate intuitive visualizations. Ultimately, ML-driven transcriptomics provides a robust technical foundation for disease diagnosis, drug discovery, and precision medicine. These advancements underscore the pivotal role of ML in transforming transcriptomic data analysis into an intelligent, highly precise, and multidimensional discipline, thereby accelerating future biological discoveries.
Zhu et al. (2026) studied this question.