Graph neural networks

G Corso, H Stark, S Jegelka, T Jaakkola… - Nature Reviews …, 2024 - nature.com
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 …

Artificial intelligence for science in quantum, atomistic, and continuum systems

X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie… - arXiv preprint arXiv …, 2023 - arxiv.org
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 …

Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification

X Lin, W Zhang, F Shi, C Zhou, L Zou… - … on Machine Learning, 2024 - openreview.net
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 …

Zero-shot drug repurposing with geometric deep learning and clinician centered design

K Huang, P Chandak, Q Wang, S Havaldar, A Vaid… - medRxiv, 2023 - medrxiv.org
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 …

Conformalized Link Prediction on Graph Neural Networks

T Zhao, J Kang, L Cheng - Proceedings of the 30th ACM SIGKDD …, 2024 - dl.acm.org
Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes
domains are often hampered by unreliable predictions. Although numerous uncertainty …

Contextual AI models for single-cell protein biology

MM Li, Y Huang, M Sumathipala, MQ Liang… - Nature …, 2024 - nature.com
Understanding protein function and developing molecular therapies require deciphering the
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

S Park, KM Cohen, O Simeone - IEEE Transactions on Pattern …, 2023 - ieeexplore.ieee.org
Conventional frequentist learning is known to yield poorly calibrated models that fail to
reliably quantify the uncertainty of their decisions. Bayesian learning can improve …

A foundation model for clinician-centered drug repurposing

K Huang, P Chandak, Q Wang, S Havaldar, A Vaid… - Nature Medicine, 2024 - nature.com
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 …

Contextualizing protein representations using deep learning on protein networks and single-cell data

MM Li, Y Huang, M Sumathipala, MQ Liang… - bioRxiv, 2023 - biorxiv.org
Understanding protein function and developing molecular therapies require deciphering the
cell types in which proteins act as well as the interactions between proteins. However …

Uncertainty in Graph Neural Networks: A Survey

F Wang, Y Liu, K Liu, Y Wang, S Medya… - arXiv preprint arXiv …, 2024 - arxiv.org
Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …