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Found 6 papers, 6 papers with code
3D-based RNA function prediction tools in rnaglib
Understanding the connection between complex structural features of RNA and biological function is a fundamental challenge in evolutionary studies and in RNA design.
Endowing Protein Language Models with Structural Knowledge
Drawing from recent advances in graph transformers, our approach refines the self-attention mechanisms of pretrained language transformers by integrating structural information with structure extractor modules.
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis into a joint optimization problem to simultaneously generate isometric embeddings of data and learn correspondences between instances from two different datasets, while only requiring intra-dataset pairwise dissimilarities as input.
Approximate Network Motif Mining Via Graph Learning
Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets.
RNAglib: A Python Package for RNA 2.5D Graphs
RNA 3D architectures are stabilized by sophisticated networks of (non-canonical) base pair interactions, which can be conveniently encoded as multi-relational graphs and efficiently exploited by graph theoretical approaches and recent progresses in machine learning techniques.
VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs
State of the art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space.
