516 citations · 647 across the 12 of their papers we have counts for
13 papers
VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
Florian Sestak, Lisa Schneckenreiter, Johannes Brandstetter +3
Being able to identify regions within or around proteins, to which ligands can potentially bind, is an essential step to develop new drugs. Binding site identification methods can…
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak +3
Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling. The expressivity of…
Redundancy-aware unsupervised rankings for collections of gene sets
Chiara Balestra, Carlo Maj, Emmanuel Müller +1
The biological roles of gene sets are used to group them into collections. These collections are often characterized by being high-dimensional, overlapping, and redundant families…
Redundancy-aware unsupervised ranking based on game theory -- application to gene enrichment analysis
Chiara Balestra, Carlo Maj, Emmanuel Mueller +1
Gene set collections are a common ground to study the enrichment of genes for specific phenotypic traits. Gene set enrichment analysis aims to identify genes that are over-represen…
Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data
Chiara Balestra, Florian Huber, Andreas Mayr +1
Not all real-world data are labeled, and when labels are not available, it is often costly to obtain them. Moreover, as many algorithms suffer from the curse of dimensionality, red…
Boundary Graph Neural Networks for 3D Simulations
Andreas Mayr, Sebastian Lehner, Arno Mayrhofer +3
The abundance of data has given machine learning considerable momentum in natural sciences and engineering, though modeling of physical processes is often difficult. A particularly…