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Updated: Jun 8, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
Published on: May 31, 2013
Predicting RNA structures and functions by artificial intelligence
Jun Zhang1, Mei Lang2, Yaoqi Zhou2
1National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
Abstract:
RNA functions by interacting with its intended targets structurally. However, due to the dynamic nature of RNA molecules, RNA structures are difficult to determine experimentally or predict computationally. Artificial intelligence (AI) has revolutionized many biomedical fields and has been progressively utilized to deduce RNA structures, target binding, and associated functionality. Integrating structural and target binding information could also help improve the robustness of AI-based RNA function prediction and RNA design. Given the rapid development of deep learning (DL) algorithms, AI will provide an unprecedented opportunity to elucidate the sequence-structure-function relation of RNAs.
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