1 code implementation • 14 Feb 2024 • Carlos Oliver, Vincent Mallet, Jérôme Waldispühl
Understanding the connection between complex structural features of RNA and biological function is a fundamental challenge in evolutionary studies and in RNA design.
1 code implementation • 28 Sep 2023 • Vincent Mallet, Souhaib Attaiki, Maks Ovsjanikov
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.
1 code implementation • 2 Jun 2022 • Carlos Oliver, Dexiong Chen, Vincent Mallet, Pericles Philippopoulos, Karsten Borgwardt
Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets.
1 code implementation • NeurIPS 2021 • Vincent Mallet, Jean-Philippe Vert
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.
1 code implementation • 20 Sep 2021 • Vincent Mallet, Carlos G. Oliver, William L. Hamilton
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.
1 code implementation • 9 Sep 2021 • Vincent Mallet, Carlos Oliver, Jonathan Broadbent, William L. Hamilton, Jérôme Waldispühl
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.
2 code implementations • 1 Sep 2020 • Carlos Oliver, Vincent Mallet, Pericles Philippopoulos, William L. Hamilton, Jerome Waldispuhl
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.
no code implementations • 28 May 2019 • Vincent Mallet, Carlos G. Oliver, Nicolas Moitessier, Jerome Waldispuhl
We use the 3D structure of the binding site as input to a model which predicts the ligand preferences of the binding site.