Machine learning models exhibit strong performance on datasets with abun...
Graph Neural Networks (GNNs) are powerful machine learning prediction mo...
Interactive data exploration (IDE) is an effective way of comprehending ...
Despite many advances in Graph Neural Networks (GNNs), their training
st...
Thanks to the increasing availability of genomics and other biomedical d...
With the remarkable success of representation learning in providing powe...
Clinical trials are crucial for drug development but are time consuming,...
Unstructured clinical text in EHRs contains crucial information for
appl...
The efficacy of a drug depends on its binding affinity to the therapeuti...
Thanks to the increasing availability of drug-drug interactions (DDI)
da...
Prevailing methods for graphs require abundant label and edge informatio...
Molecular interaction networks are powerful resources for the discovery....
Drug target interaction (DTI) prediction is a foundational task for in s...
We present DeepPurpose, a deep learning toolkit for simple and efficient...
Clinical notes contain rich data, which is unexploited in predictive mod...
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidit...
Clinical notes contain information about patients that goes beyond struc...
Epistasis (gene-gene interaction) is crucial to predicting genetic disea...