Search Results for author:
Found 6 papers, 2 papers with code
Graph AI in Medicine
With diverse data -- from patient records to imaging -- GNNs process data holistically by viewing modalities as nodes interconnected by their relationships.
Current and future directions in network biology
As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.
Discrepancies in Epidemiological Modeling of Aggregated Heterogeneous Data
We evaluate two data-generating models within this Bayesian inference framework: a simple exponential growth model and a highly flexible Gaussian process prior model.
Deep Contextual Learners for Protein Networks
We construct a multi-scale network of the Human Cell Atlas and apply AWARE to learn protein, cell type, and tissue embeddings that uphold cell type and tissue hierarchies.
Graph Representation Learning in Biomedicine
Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge.
Subgraph Neural Networks
Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks.
