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Found 8 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.
AtomSurf : Surface Representation for Learning on Protein Structures
An essential aspect of learning from protein structures is the choice of their representation as a geometric object (be it a grid, graph, or surface), which conditions the associated learning method.
Approximate Network Motif Mining Via Graph Learning
Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets.
Reverse-Complement Equivariant Networks for DNA Sequences
As DNA sequencing technologies keep improving in scale and cost, there is a growing need to develop machine learning models to analyze DNA sequences, e. g., to decipher regulatory signals from DNA fragments bound by a particular protein of interest.
Edge-similarity-aware Graph Neural Networks
For instance, the 3D structure of RNA can be efficiently represented as $\textit{2. 5D graphs}$, graphs whose nodes are nucleotides and edges represent chemical interactions.
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.
Leveraging binding-site structure for drug discovery with point-cloud methods
We use the 3D structure of the binding site as input to a model which predicts the ligand preferences of the binding site.
