1 citations · 1 across the 4 of their papers we have counts for
5 papers
ARO: Aligned Representation learning for multi-Omics data
Amogh Singh, Yash Shah, Chiara D'Ercoli +4
The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant barriers to comprehensive mult…
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…
Decoding the Stressed Brain with Geometric Machine Learning
Sonia Koszut, Sam Nallaperuma-Herzberg, Pietro Lio
Stress significantly contributes to both mental and physical disorders, yet traditional self-reported questionnaires are inherently subjective. In this study, we introduce a novel…
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…