Journal Article

FZJ-2023-04901

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png RNA contact prediction by data efficient deep learning

Taubert, O. ; von der Lehr, F. ; Bazarova, A.FZJ* ; Faber, C.FZJ* ; Knechtges, P. ; Weiel, M. ; Debus, C. ; Coquelin, D. ; Basermann, A. ; Streit, A. ; Kesselheim, S.FZJ* ; Götz, M. ; Schug, A. (Corresponding author)FZJ*

2023
Springer Nature London

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Please use a persistent id in citations: doi:10.1038/s42003-023-05244-9  doi:10.34734/FZJ-2023-04901

Abstract: On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps”) as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
3. HAF - Helmholtz Analytics Framework (ZT-I-0003) (ZT-I-0003)
4. Helmholtz AI Consultant Team FB Information (E54.303.11) (E54.303.11)

Appears in the scientific report 2023

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