Di He

Binding Site-enhanced Sequence Pretraining and Out-of-cluster Meta-learning Predict Genome-Wide Chemical-Protein Interactions for Dark Proteins

PortalCG predicts chemical-protein interactions for dark proteins using a deep learning framework that integrates binding site information and meta-learning, outperforming existing methods in out-of-distribution scenarios.

Robust Prediction of Patient-Specific Clinical Response to Unseen Drugs From in vitro Screens Using Context-aware Deconfounding Autoencoder

The paper introduces CODE-AE, a context-aware deconfounding autoencoder, to predict patient-specific drug responses from in vitro screens and discovers personalized therapies and biomarkers.

Rational Discovery of Dual-Action Multi-Target Kinase Inhibitor for Precision Anti-Cancer Therapy Using Structural Systems Pharmacology

The paper develops a multi-target screening platform to discover dual-action drugs for cancer therapy, identifying levosimendan as a potential candidate that reduces heart failure risk and inhibits cancer growth.

Identifying Context-specific Network Features for CRISPR-Cas9 Targeting Efficiency Using Accurate and Interpretable Deep Neural Network

The paper develops an interpretable deep learning model to improve CRISPR-Cas9 sgRNA prediction by incorporating gene network context.

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

JDINAC identifies differential interaction networks and classifies diseases using high-dimensional sparse omics data without parametric assumptions, achieving accurate results in breast cancer analysis.