ΑΙhub.org
 

Machine learning tool may help us better understand RNA viruses

rna_secondary_structure
E2Efold is an end-to-end deep learning model developed at Georgia Tech that can predict RNA secondary structures, an important task used in virus analysis, drug design, and other public health applications.

Although the model has yet to be used in real-life applications, in research testing it has shown at least a 10 percent improvement in structure prediction accuracy compared to previous state-of-the-art methods according to Xinshi Chen, a Georgia Tech Ph.D. student specializing in machine learning and co-developer of the new tool.

“The model uses an unrolled algorithm for solving a constrained optimization as a component in the neural network architecture, so that it can directly incorporate a solution constraint, or prior knowledge, to predict the RNA base-pairing matrix,” said Chen.

E2Efold is not only more accurate, it is also considerably faster than current techniques.

Current methods are dynamic programming based, which is a much slower approach for predicting longer RNA sequences, such as the genomic RNA in a virus. E2Efold overcomes this drawback by using a gradient-based unrolled algorithm. It also takes advantage of graphic processing units to accelerate its computing process and is now the fastest method available.

RNA, or ribonucleic acid, is an essential building block that governs gene expression and is particularly important for RNA viruses, which consist only of RNA and the enwrapping virion proteins. These types of viruses make up a wide array of infectious diseases, including SARS, Dengue fever, the common cold, and others.

“Unlike most organisms, the genetic information of an RNA virus is RNA. As a result, almost every stage in the RNA virus life cycle relies on RNA heavily,” said Yu Li, a computational bioscience researcher from King Abdullah University of Science and Technology (KAUST) and co-investigator.

“Take SARS, as an example. It belongs to an RNA virus. If we can predict its secondary and 3D structure accurately, based on its sequence information, we can potentially design drugs to bind to its local binding pocket and block the RNA from functioning. In other words, researchers might be able to develop treatments for the virus based on the specific local structure of the target RNA using this method as a starting point,” said Li.

One additional noteworthy ability of E2Efold is its ability to solve for pseudoknots. Pseudoknots are a biologically important RNA secondary structure that are present in roughly 40 percent of RNAs and assist with folding into 3D structures.

RNA nested structure and pseudoknot

“Most previous models were restricted to only predict one type of RNA structure called nested structures. This excluded pseudoknots all together because they were computationally expensive,” said Chen. “In this paper, we predict RNA structures with pseudoknots by adopting a feed-forward model with a 25 percent greater accuracy than previous versions.”

Led by Georgia Tech School of Computational Science and Engineering (CSE) Associate Professor Le Song and KAUST Associate Professor Xin Gao, the team of researchers who created the model will present the paper outlining their findings at the International Conference on Learning Representations (ICLR) 2020.

Although the focus of the paper is on RNA secondary prediction, E2Efold’s end-to-end deep learning approach is generic enough to also be applied to other problems such as protein folding and natural language understanding.




Machine Learning Center at Georgia Tech




            AIhub is supported by:


Related posts :



monthly digest

AIhub monthly digest: May 2025 – materials design, object state classification, and real-time monitoring for healthcare data

  30 May 2025
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners

  29 May 2025
Find out who won the awards presented at the International Conference on Autonomous Agents and Multiagent Systems last week.

The Good Robot podcast: Transhumanist fantasies with Alexander Thomas

  28 May 2025
In this episode, Eleanor talks to Alexander Thomas, a filmmaker and academic, about the transhumanist narrative.

Congratulations to the #ICRA2025 best paper award winners

  27 May 2025
The winners and finalists in the different categories have been announced.

#ICRA2025 social media round-up

  23 May 2025
Find out what the participants got up to at the International Conference on Robotics & Automation.

Interview with Gillian Hadfield: Normative infrastructure for AI alignment

  22 May 2025
Kumar Kshitij Patel spoke to Gillian Hadfield about her interdisciplinary research, career trajectory, path into AI alignment, law, and general thoughts on AI systems.

PitcherNet helps researchers throw strikes with AI analysis

  21 May 2025
Baltimore Orioles tasks Waterloo Engineering researchers to develop AI tech that can monitor pitchers using low-resolution video captured by smartphones

Interview with Filippos Gouidis: Object state classification

  20 May 2025
Read the latest interview in our series featuring the AAAI/SIGAI Doctoral Consortium participants.



 

AIhub is supported by:






©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence