AI-driven approaches in therapeutic interventions: Transforming RNA-seq analysis into biomarker discovery and drug development.
Journal:
Drug discovery today
Published Date:
May 29, 2025
Abstract
Pharmacotranscriptomics integrates transcriptomics and pharmacology to discover potential therapeutic targets for effective treatment. This review focuses on significant advancements in combining artificial intelligence (AI) with transcriptomic research, enabling the conversion of vast data sets into valuable knowledge for for developing effective therapeutics. We provide detailed insights into implementing machine learning (ML) techniques for analyzing intricate transcriptomic data, facilitating a comprehensive understanding of disease mechanisms and the identification of key signature genes for biomarker and drug development. We further highlighted the potential of ML to streamline the drug discovery process by revealing disease mechanisms and suggesting therapeutic interventions. This review presents a comprehensive framework of AI models and their applications within pharmacotranscriptomics analysis. We also discuss the challenges and limitations needed to optimize AI models for enhanced therapeutic outcomes.
Authors
Keywords
No keywords available for this article.
