Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.
The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computational approaches have emerged as valuable tools, le...
Saved in:
Main Authors: | Akash Bahai, Chee Keong Kwoh, Yuguang Mu, Yinghui Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2024-12-01
|
Series: | PLoS Computational Biology |
Online Access: | https://doi.org/10.1371/journal.pcbi.1012715 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort
by: Yifan Jiang, et al.
Published: (2025-01-01) -
Synthetic graphs for link prediction benchmarking
by: Alexey Vlaskin, et al.
Published: (2025-01-01) -
Identifying the Factors Affecting Student Academic Performance and Engagement Prediction in MOOC Using Deep Learning: A Systematic Literature Review
by: Shahzad Rizwan, et al.
Published: (2025-01-01) -
NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation
by: Yuki Kagaya, et al.
Published: (2025-01-01) -
Benchmarking Pain Outcomes for Children with Sickle Cell Disease Hospitalized in a Tertiary Referral Pediatric Hospital
by: Abi Vijenthira, et al.
Published: (2012-01-01)