Zhu‐Hong You

The paper proposes iGRLDTI, a graph representation learning method for drug-target interaction prediction, achieving better performance than existing models on benchmark datasets.

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.

This paper introduces MeSHHeading2vec, a method that represents MeSH headings as vectors using graph embedding algorithms, showing improved performance in node classification and relationship prediction tasks.

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

GNMFLMI predicts lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization, achieving high accuracy and outperforming existing methods.

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.

The paper introduces Biomarker2vec, a method that predicts multi-type relationships between biomarkers using a heterogeneous network and vector representation, achieving high performance on 18 relationships.

The paper constructs a large-scale molecular associations network integrating multiple biomolecules and predicts interactions with high accuracy.