Lei Wang

SPRDA predicts disease-associated piRNAs using a matrix completion approach based on structural perturbation, achieving high performance on benchmark datasets.

GCNCDA predicts circRNA-disease associations using a graph convolutional network, achieving high accuracy and confirming predictions with literature.

RFDTI predicts drug-target interactions using a feature-weighted Rotation Forest classifier, achieving high accuracy on benchmark datasets and validating novel interactions with databases.

This paper introduces GAPDA, a graph attention network method for identifying piRNA-disease associations, achieving high AUC in cross-validation and outperforming existing methods.

The paper introduces iMDA-BN, a computational model for predicting miRNA-disease associations using biological networks and graph embedding, achieving high accuracy on benchmark datasets.