Deep Learning for RNA Regulation and Multidimensional Transcriptomics (Dagstuhl Seminar 24491)

Abstract

The Dagstuhl Seminar 24491 "Deep Learning for RNA Regulation and Multidimensional Transcriptomics" convened experts from computer science, computational biology, and experimental research to explore the intersection of artificial intelligence and RNA biology. The seminar facilitated discussions on the latest computational methods and experimental approaches that are reshaping our understanding of RNA-mediated gene regulation. With the rapid growth of transcriptomics data, deep learning methods are becoming essential tools for extracting insights from complex datasets, ranging from primary sequence information to intricate cellular dynamics.
A key theme of the seminar was the exploration of non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs, which play pivotal roles in regulating gene expression. High-throughput methods to profile these RNAs, combined with deep learning algorithms, are enabling the identification of novel regulatory mechanisms and the prediction of their cellular functions. The discussion underscored the challenges in classifying lncRNAs, deciphering their sequence features, and understanding their functional interactions.
The seminar also addressed the integration of deep learning in modeling RNA regulatory networks. Participants presented cutting-edge models for predicting RNA modifications, RNA-protein interactions, and the effects of genetic variants on RNA metabolism. Special attention was given to the interpretability of machine learning models, as understanding the biological significance of predictions remains a critical challenge. Advances in single-cell and spatial transcriptomics were highlighted as key drivers of future breakthroughs, offering unprecedented resolution of cellular heterogeneity and regulatory processes.
Another major focus was the role of deep learning in RNA-based therapeutic development. Discussions included the use of machine learning for designing RNA sequences in synthetic biology applications, predicting the efficacy of antisense oligonucleotides (ASOs), and identifying cancer-specific neoantigens. These applications demonstrate the potential of AI to accelerate the discovery of novel RNA-targeted therapies and improve precision medicine approaches.
In addition, the seminar emphasized the importance of community-driven initiatives to improve benchmarking, data curation, and collaborative model development. Participants highlighted the need for standardized datasets, transparent evaluation metrics, and shared computational resources to foster reproducibility and innovation. The discussions underscored the necessity of cross-disciplinary collaboration to ensure that machine learning methods address biologically meaningful questions and produce actionable insights.
Overall, the seminar illustrated how deep learning is transforming RNA biology by uncovering new layers of gene regulation and facilitating therapeutic discoveries. Moving forward, continued interdisciplinary collaboration and the development of scalable, interpretable models will be essential to unlock the full potential of AI in decoding RNA functions and advancing biomedical research.