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Found 4 papers, 3 papers with code
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction
Owing to the scarcity of labeled molecules, there has been growing interest in self-supervised learning methods that learn generalizable molecular representations from unlabeled data.
Interpretable bilinear attention network with domain adaptation improves drug-target prediction
Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation; (ii) how to generalize prediction performance on novel drug-target pairs from different distribution.
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems.
GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks.
