Graph neural networks
Graphs are flexible mathematical objects that can represent many entities and knowledge
from different domains, including in the life sciences. Graph neural networks (GNNs) are …
from different domains, including in the life sciences. Graph neural networks (GNNs) are …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
Graph neural networks (GNNs) have advanced the state of the art in various domains.
Despite their remarkable success, the uncertainty estimation of GNN predictions remains …
Despite their remarkable success, the uncertainty estimation of GNN predictions remains …
Zero-shot drug repurposing with geometric deep learning and clinician centered design
Of the several thousand diseases that affect humans, only about 500 have treatments
approved by the US Food and Drug Administration. Even for those with approved …
approved by the US Food and Drug Administration. Even for those with approved …
Conformalized Link Prediction on Graph Neural Networks
Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes
domains are often hampered by unreliable predictions. Although numerous uncertainty …
domains are often hampered by unreliable predictions. Although numerous uncertainty …
Contextual AI models for single-cell protein biology
Understanding protein function and developing molecular therapies require deciphering the
cell types in which proteins act as well as the interactions between proteins. However …
cell types in which proteins act as well as the interactions between proteins. However …
Few-shot calibration of set predictors via meta-learned cross-validation-based conformal prediction
Conventional frequentist learning is known to yield poorly calibrated models that fail to
reliably quantify the uncertainty of their decisions. Bayesian learning can improve …
reliably quantify the uncertainty of their decisions. Bayesian learning can improve …
A foundation model for clinician-centered drug repurposing
Drug repurposing—identifying new therapeutic uses for approved drugs—is often a
serendipitous and opportunistic endeavour to expand the use of drugs for new diseases …
serendipitous and opportunistic endeavour to expand the use of drugs for new diseases …
Contextualizing protein representations using deep learning on protein networks and single-cell data
Understanding protein function and developing molecular therapies require deciphering the
cell types in which proteins act as well as the interactions between proteins. However …
cell types in which proteins act as well as the interactions between proteins. However …
Uncertainty in Graph Neural Networks: A Survey
Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …