Exploring RNA Biology with Deep Learning Algorithms

The RNA biology journal is launching a new open‑access article collection called “Exploring RNA Biology with Deep Learning Algorithms” to bring together the latest breakthroughs at the intersection of transcriptomics, RNA structure prediction, molecular design and AI‑driven approaches.

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 showcase 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.

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Why This Collection Matters

RNA plays a central role in virtually every biological process, from gene regulation to the assembly of protein complexes. Yet its complexity poses formidable analytical challenges. Over the past few years, deep learning has transformed the way we decode protein structures and genomic data. Now, we stand on the edge of a similar revolution in RNA biology:

  • 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:

Details & Submission

Join the Conversation

Have questions about a potential submission, or want to discuss a cutting‑edge idea? Feel free to reach out via the contact form on this site or connect with me on LinkedIn.

Xinyang flavivirus is a novel tick-borne Orthoflavivirus

In this paper, we report the discovery of Xinyang flavivirus (XiFV), a novel virus isolated from Haemaphysalis flava ticks in China. Through phylogenetic analysis, we found that XiFV is closely related to other tick-only flaviviruses, such as Mpulungu flavivirus (MPFV) from Zambia and Ngoye virus (NGOV) from Senegal. This positions XiFV in a unique clade of orthoflaviviruses, a group that seems to infect only ticks, without any known vertebrate host

The pivotal role of virus bioinformatics in global health

On One Health Day, we recognize the intricate links between human, animal, and environmental health, underscored by the emergence of novel viruses like SARS-CoV-2. This post, crafted for a lay audience, delves into the critical contributions of virus bioinformatics in understanding and combating infectious diseases. It emphasizes the importance of predicting RNA structures for understanding RNA viruses and discusses the role of genomic epidemiology in tracking viral spread and evolution

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