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Updated: Jun 13, 2025

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
Published on: August 4, 2016
AI-driven approaches in therapeutic interventions: Transforming RNA-seq analysis into biomarker discovery and drug
Zehra1, Anam Bakhtiyar2, Asimul Islam1
1Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India.
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.
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