7 citations · 9 across the 5 of their papers we have counts for
5 papers
MoRE-GNN: Multi-omics Data Integration with a Heterogeneous Graph Autoencoder
Zhiyu Wang, Sonia Koszut, Pietro Liò +1
The integration of multi-omics single-cell data remains challenging due to high-dimensionality and complex inter-modality relationships. To address this, we introduce MoRE-GNN (Mul…
ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data
Santanu Subhash Rathod, Francesco Ceccarelli, Sean B. Holden +3
Inferring trajectories from longitudinal spatially-resolved omics data is fundamental to understanding the dynamics of structural and functional tissue changes in development, rege…
Neural Embeddings for Protein Graphs
Francesco Ceccarelli, Lorenzo Giusti, Sean B. Holden +1
Proteins perform much of the work in living organisms, and consequently the development of efficient computational methods for protein representation is essential for advancing lar…
CIN++: Enhancing Topological Message Passing
Lorenzo Giusti, Teodora Reu, Francesco Ceccarelli +2
Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, they face significant limitations in expressive power, struggling…
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation
Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli +1
It is fundamental for science and technology to be able to predict chemical reactions and their properties. To achieve such skills, it is important to develop good representations…