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RNA Secondary Structure Prediction By Learning Unrolled Algorithms

ICLR 2020 · Probabilistic and scientific modeling › Molecules and proteins · approach: Generative latent models for single-cell data · 36 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
integrate the post-processing network into training by backpropagating through it (ablation)E2Efold · RNAStralign test setF1: 0.721 -> 0.821Helped
integrate the post-processing network into training by backpropagating through it (ablation)E2Efold · RNAStralign test setprecision: 0.755 -> 0.866Helped
integrate the post-processing network into training by backpropagating through it (ablation)E2Efold · RNAStralign test setrecall: 0.712 -> 0.788Helped
integrate the post-processing network into training by backpropagating through it (ablation)E2Efold · RNAStralign test setF1(S): 0.752 -> 0.833Helped
use E2Efold rather than CONTRAfoldRNA secondary structure prediction · ArchiveIIF1: 0.638 -> 0.686Helped
use E2Efold rather than CDPfoldRNA secondary structure prediction · RNAStralignF1: 0.614 -> 0.821Helped
use E2Efold rather than LinearFoldRNA secondary structure prediction · RNAStraligntime per sequence: 0.40s vs 0.43sNo clear effect
test the RNAStralign-trained model on ArchiveII without retrainingE2Efold · ArchiveIIF1: 0.704 vs 0.662Helped

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