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Found 6 papers, 1 papers with code
A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction
In the drug development engineering field, predicting novel drug-target interactions is extremely crucial. However, although existing methods have achieved high accuracy levels in predicting known drugs and drug targets, they fail to utilize global protein information during DTI prediction.
Regressor-free Molecule Generation to Support Drug Response Prediction
As a result, we introduce regressor-free guidance molecule generation to ensure sampling within a more effective space and support DRP.
CLNode: Curriculum Learning for Node Classification
Experimental results on real-world networks demonstrate that CLNode is a general framework that can be combined with various GNNs to improve their accuracy and robustness.
Understanding Partial Multi-Label Learning via Mutual Information
To deal with ambiguities in partial multilabel learning (PML), state-of-the-art methods perform disambiguation by identifying ground-truth labels directly.
Fast Multi-label Learning
The goal of this paper is to provide a simple method, yet with provable guarantees, which can achieve competitive performance without a complex training process.
Online Metric Learning for Multi-Label Classification
Specifically, in order to learn the new $k$NN-based metric, we first project instances in the training dataset into the label space, which make it possible for the comparisons of instances and labels in the same dimension.
