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001018573 1001_ $$0P:(DE-HGF)0$$aTaubert, Oskar$$b0
001018573 245__ $$aRNA contact prediction by data efficient deep learning
001018573 260__ $$aLondon$$bSpringer Nature$$c2023
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001018573 520__ $$aOn 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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001018573 7001_ $$0P:(DE-HGF)0$$avon der Lehr, Fabrice$$b1
001018573 7001_ $$0P:(DE-Juel1)192120$$aBazarova, Alina$$b2$$ufzj
001018573 7001_ $$0P:(DE-Juel1)188661$$aFaber, Christian$$b3
001018573 7001_ $$0P:(DE-HGF)0$$aKnechtges, Philipp$$b4
001018573 7001_ $$0P:(DE-HGF)0$$aWeiel, Marie$$b5
001018573 7001_ $$00000-0002-7156-2022$$aDebus, Charlotte$$b6
001018573 7001_ $$0P:(DE-Juel1)177671$$aCoquelin, Daniel$$b7
001018573 7001_ $$00000-0003-3637-3231$$aBasermann, Achim$$b8
001018573 7001_ $$0P:(DE-HGF)0$$aStreit, Achim$$b9
001018573 7001_ $$0P:(DE-Juel1)185654$$aKesselheim, Stefan$$b10$$ufzj
001018573 7001_ $$0P:(DE-Juel1)162390$$aGötz, Markus$$b11
001018573 7001_ $$0P:(DE-Juel1)173652$$aSchug, Alexander$$b12$$eCorresponding author
001018573 773__ $$0PERI:(DE-600)2919698-X$$a10.1038/s42003-023-05244-9$$gVol. 6, no. 1, p. 913$$n1$$p913$$tCommunications biology$$v6$$x2399-3642$$y2023
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