AI in RNA Science

The intersection of artificial intelligence (AI) and RNA science represents a rapidly advancing frontier with the potential to revolutionize our understanding of molecular biology and disease mechanisms. RNA, a critical molecule in diverse biological processes, acts as a messenger, a catalyst, and a regulatory molecule. Traditional methods of studying RNA involve intricate experimental setups which are often time-consuming and costly. However, the advent of AI and machine learning techniques is transforming the way researchers analyze vast datasets, derive meaningful insights, and predict RNA behavior in various biological contexts. Machine learning algorithms can process high-dimensional RNA data, uncover patterns, and assist in the identification of novel RNA structures, dynamics, interactions, and functions. These technological advancements open up unprecedented opportunities for breakthroughs in areas such as RNA therapeutics, transcriptomics, and the understanding of RNA regulatory mechanisms. AI-driven approaches are key to overcoming the existing challenges in RNA science by providing scalable and efficient analytical tools.This research topic aims to explore and showcase the latest developments in the application of AI and machine learning techniques within RNA science. We seek to highlight innovative approaches and methodologies that enhance our understanding of RNA biology, improve RNA-based diagnostics and therapeutics, and promote the integration of AI tools within the broader field of RNA research.We invite researchers and experts in RNA-related computational biology, bioinformatics, and AI to submit their original research, reviews, methodologies, technology and code, perspectives, and case studies. Submissions should focus on applications of AI in RNA science, addressing challenges and showcasing breakthroughs. Papers will contribute to advancing the field by offering insights into the integration of AI in various aspects of RNA research. Submissions can explore but are not limited to:1. AI in RNA Structure Prediction: Contributions that develop or employ AI models to predict RNA tertiary and quaternary structures with improved accuracy.2. RNA-Protein Interaction Analysis Using AI: Studies utilizing AI to understand and predict RNA-protein interactions, enhancing the knowledge of post-transcriptional regulation.3. AI-Assisted Transcriptomics and Differential Expression Analysis: Investigations applying machine learning for the analysis of RNA-seq data to identify differential gene expression patterns with biological significance.4. AI in RNA Editing and Modification: Research focused on modeling RNA editing and chemical modification events using AI techniques.5. AI-Assisted Drug Discovery and RNA-Targeted Therapeutics: Papers exploring AI-driven approaches to identify RNA targets for drug discovery and the development of RNA-based therapeutics.6. AI-Assisted Non-coding RNA Function and Classification: Novel AI methods for the classification, annotation, and functional prediction of various non-coding RNAs.These subthemes encourage a diverse range of contributions that leverage AI to address key challenges in RNA science, fostering innovation and collaboration across the computational and experimental biology communities. Topic editors: Shuhui Song Source: https://www.frontiersin.org/research-topics/70344/ai-in-rna-science