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Artificial Intelligence in Transcriptomics: From Human-in-the-Loop to Agentic AI.

Giulia Gentile1, Giovanna Morello1, Valentina La Cognata1

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Summary
This summary is machine-generated.

Artificial Intelligence (AI) is revolutionizing transcriptomics by enabling holistic gene expression analysis. Agentic AI offers a futuristic pipeline for automated data processing and hypothesis generation, advancing personalized medicine.

Keywords:
agentic AIartificial intelligencedeep learningfunctional genomicsgenerative AImachine learningpersonalized medicineprecision medicinereinforcement learningtranscriptomics

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Area of Science:

  • Computational systems biology
  • Genomics and transcriptomics
  • Bioinformatics

Background:

  • Biological research is shifting towards holistic, genome-scale approaches like transcriptomics.
  • Artificial Intelligence (AI) is increasingly used for gene expression analysis and data integration.
  • Emerging agentic AI systems promise autonomous data processing and hypothesis generation.

Purpose of the Study:

  • To explore the potential of agentic AI in transcriptomics data analysis.
  • To propose a futuristic data analysis pipeline utilizing agentic AI.
  • To consider the implications of AI agents in research and clinical practice.

Main Methods:

  • AI learning models for gene expression analysis and integration.
  • Agentic AI for automated data retrieval, pre-processing, and analysis.
  • Incremental Updating and Recurrent Analysis (IURA) model for continuous learning and hypothesis generation.

Main Results:

  • AI models address transcriptomics data heterogeneity, integration, and updating challenges.
  • Agentic AI enables autonomous processing of transcriptomics and other omics data.
  • The IURA model facilitates detection of guideline updates and generation of novel hypotheses (e.g., biomarkers, correlations).

Conclusions:

  • Agentic AI presents a transformative approach to transcriptomics research.
  • Automated analysis pipelines can enhance data integration and accelerate scientific discovery.
  • The integration of AI agents in research and clinical practice offers significant benefits for personalized medicine but requires careful consideration of associated advantages and concerns.