Area of research
Molecular Biology · Computational Theory and Mathematics
Research interest
Research interests include Computational Drug Discovery Methods, Bioinformatics and Genomic Networks, Machine Learning in Bioinformatics, and Cancer-related molecular mechanisms research.
LLM-DDI: Leveraging Large Language Models for Drug-Drug Interaction Prediction on Biomedical Knowledge Graph.
Leveraging 3D Molecular Spatial Visual Information and Multi-Perspective Representations for Drug Discovery.
Multi-View Contrastive Learning for Drug-Drug Interaction Event Prediction.
Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning
Leveraging 3D Molecular Spatial Visual Information and Multi‐Perspective Representations for Drug Discovery
Toward Multilabel Classification for Multiple Disease Prediction Using Gut Microbiota Profiles
A knowledge-driven deep learning framework for organoid morphological segmentation and characterization
Searching for an Accurate Robot Calibration via Improved Levenberg–Marquardt and Radial Basis Function System
Knowledge Graph Neural Network With Spatial-Aware Capsule for Drug-Drug Interaction Prediction.
Dual-Channel MiRNA Drug Resistance Prediction Model Based on Multimodal Feature Alignment
Dual-Channel Learning Framework for Drug-Drug Interaction Prediction via Relation-Aware Heterogeneous Graph Transformer
Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering.
Motif-Aware miRNA-Disease Association Prediction via Hierarchical Attention Network.
Molecular epidemiology and population immunity of SARS-CoV-2 in Guangdong (2022–2023) following a pivotal shift in the pandemic
IDHPre: Intradialytic Hypotension Prediction Model Based on Fully Observed Features
GGANet: A Model for the Prediction of MiRNA-Drug Resistance Based on Contrastive Learning and Global Attention
DNMDA: Deep Non-negative Matrix Factorization with Multi-level Integration for MiRNA-Drug Interaction Prediction
Predicting Drug-Target Interactions Over Heterogeneous Information Network.
PPAEDTI: Personalized Propagation Auto-Encoder Model for Predicting Drug-Target Interactions.
De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning
Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder.
STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks
Investigating the relationships between emotional experiences and behavioral responses amid the Covid-19 pandemic: A cross-sectional survey.
Health Beliefs, Trust in Media Sources, Health Literacy, and Preventive Behaviors among High-Risk Chinese for COVID-19.
Identifying Protein Complexes From Protein-Protein Interaction Networks Based on Fuzzy Clustering and GO Semantic Information.
Learning from low-rank multimodal representations for predicting disease-drug associations.
Chinese Public's Engagement in Preventive and Intervening Health Behaviors During the Early Breakout of COVID-19: Cross-Sectional Study.
Learning Multimodal Networks From Heterogeneous Data for Prediction of lncRNA-miRNA Interactions.
A Cross-Cultural Comparison of an Extended Planned Risk Information Seeking Model on Mental Health Among College Students: Cross-Sectional Study.