GoForth is a forward-trained encoder-decoder RNA language model that generates sequences under mixed constraints on fold, sequence, and coding by separating sequence prior, forward folding sampler, and reward oracle.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
This review surveys current machine learning methods for RNA secondary structure prediction, identifies a generalization crisis prompting homology-aware benchmarking, and outlines future challenges including pseudoknots, long transcripts, modified nucleotides, and dynamic ensembles.
citing papers explorer
-
GoForth: Language Models for RNA Design under Structure, Sequence, and Coding Constraints
GoForth is a forward-trained encoder-decoder RNA language model that generates sequences under mixed constraints on fold, sequence, and coding by separating sequence prior, forward folding sampler, and reward oracle.
-
Machine Learning for RNA Secondary Structure Prediction: a review of current methods and challenges
This review surveys current machine learning methods for RNA secondary structure prediction, identifies a generalization crisis prompting homology-aware benchmarking, and outlines future challenges including pseudoknots, long transcripts, modified nucleotides, and dynamic ensembles.