What is it about?
This work explores the use of AI agents in transcriptomics data analysis to address the Big Data issues related to the 15% annual growth of highly heterogeneous transcriptomic data in databases. At the same time, it introduces the incremental updating and recurrent analysis (IURA) model to keep transcriptomic data up to date and cyclically reanalyze it.
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Why is it important?
Human analytical capacity can be amplified by introducing Agentic AI, since these (semi-)autonomous agents can proactively manage the overwhelming volume of transcriptomic data, orchestrate end-to-end pipelines under human supervision, and could integrate the IURA architecture into the analysis pipeline.
Perspectives
The transition of AI from a data analysis tool to a "co-scientist" capable of dynamic reasoning opens new perspectives and ethical questions about the need for human supervision, moving toward a future where digital twins simulate disease progression and treatment. In addition, the application of the IURA model could allow the detection of guideline updates (e.g., disease reclassification) and the generation of new hypotheses, such as candidate biomarkers or transcriptome–phenotype correlations. This aligns with the D3 4 Health initiative, ensuring that scientific discovery translates into global health and bringing digital innovation into a predictive, sustainable, and data-driven clinical practice.
Giulia Gentile
Consiglio Nazionale delle Ricerche
Read the Original
This page is a summary of: Artificial Intelligence in Transcriptomics: From Human-in-the-Loop to Agentic AI, Journal of Personalized Medicine, March 2026, MDPI AG,
DOI: 10.3390/jpm16040181.
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