Machine Learning and AI Tools for RNA Bioinformatics

The field of RNA bioinformatics is undergoing a transformative era marked by substantial advancements in sequencing technologies and data acquisition processes. This has led to an explosion of data related to RNA biology, ranging from transcriptomics to exploring RNA structure and function, necessitating the development of sophisticated computational methods that can effectively manage and interpret this influx of information. Artificial Intelligence (AI) emerges as a powerful asset, offering the potential to discern complex patterns and automate decision-making processes, thus significantly impacting the landscape of RNA data analysis and interpretation. Recent studies demonstrate AI's capabilities, notably in leveraging machine/deep learning to improve the precision of RNA sequence alignments and advance the prediction of RNA structures, yet numerous challenges remain unresolved, highlighting an opportunity for further exploration in computational tool development.This Research Topic aims to delve into the integration of AI and computational toolsets within RNA bioinformatics to elucidate the potential of machine and deep learning approaches in overcoming existing hurdles in the field. By targeting the utilization of advanced methodologies such as large language models (LLMs), this exploration seeks to innovate in areas from RNA sequence analysis and structure prediction to functional annotation of RNA. The goal is to bridge the current technological gap by fostering groundbreaking AI and computational solutions that address key questions in RNA science, thereby enriching our understanding and application of RNA-related data.To gather further insights into AI-driven computational methods in RNA bioinformatics, we welcome articles addressing, but not limited to, the following themes:Development and integration of computational toolsets for RNA sequence analysisAI and ML/DL techniques for enhanced RNA structure predictionInnovative computational models for RNA functional annotationAI applications in RNA-related disease diagnostics and therapeutic strategiesBenchmarking and application of computational methodologies to real-world RNA science challengesWe encourage submissions of original research articles, reviews, methods, and perspectives that showcase cutting-edge developments in AI and computational strategies, ensuring a keen focus on biological relevance within the field of RNA bioinformatics. Topic editors: Ganesh Panzade, Aamir W Khan Source: https://www.frontiersin.org/research-topics/71277/machine-learning-and-ai-tools-for-rna-bioinformatics