An RNA Biology article collection on deep learning methods in transcriptomics, RNA structure prediction, and molecular design.
I will be serving as Guest Editor for the RNA Biology article collection “Exploring RNA Biology with Deep Learning Algorithms”. My aim is to curate articles that show how machine learning models can reveal hidden patterns in sequencing data, predict complex three dimensional RNA shapes with high accuracy and guide the design of novel RNA molecules for both research and therapeutic use.
What the collection is about
RNA is involved in gene regulation, in the assembly of protein complexes and in most other cellular processes. It is hard to analyse: sequence alone rarely determines function, and structure is dynamic. Deep learning has changed how protein structures and genomic data are analysed, and the same methods are now being applied to RNA. This collection asks what they are good for:
- Uncover hidden patterns in high‑throughput sequencing data
- Predict RNA modifications and their functional impacts
- Model secondary & tertiary structures with unprecedented accuracy
- Design synthetic RNAs for therapeutics and synthetic biology
- Map RNA–protein interactions and regulatory switches at scale
What We’re Looking For
By pooling insights from biochemists, computational biologists and AI specialists, this special issue aims to chart the next frontier in RNA research. We welcome original research, methods papers and in‑depth reviews on topics including (but not limited to):
- Deep learning for transcriptome analysis
- AI models of RNA modifications
- Secondary & tertiary structure prediction
- AI‑driven RNA design & editing
- RNA–protein interaction mapping
- Automated annotation of RNA architectures
Advisory Panel
I’m pleased to be joined by two leading experts in RNA science as Guest Advisors:
- Prof. Yiliang Ding (John Innes Centre): Pioneer of in vivo RNA structure mapping and chemical probing methods
- Prof. Qiangfeng Cliff Zhang (Tsinghua University): Specialist in AI‑informed big‑data analysis
What such models do and do not learn from the available structure data, and how to evaluate them, is the subject of AI for RNA structure prediction: what deep learning models learn.
Join the Conversation
Questions about a potential submission or a related research idea are always welcome. You can reach me through the contact form or connect with me on LinkedIn.