Area of research
Artificial Intelligence · Molecular Biology
Research interest
Research interests include Computer science, Feature (linguistics), Artificial intelligence, Computational biology, Phenotype, and Machine learning.
MFCADTI: improving drug-target interaction prediction by integrating multiple feature through cross attention mechanism
HPOseq: a deep ensemble model for predicting the protein-phenotype relationships based on protein sequences
MFF-HPO: Protein–Phenotype Associations Prediction Based on Sequence Using Multi-Feature Fusion
MOGATFF: An Explainable Multi-Omics Prediction Model with Feature Enhancement for Genotype-Phenotype Association Analysis
Personalized Federated Learning Based on Feature Filtering
ESGC-MDA: Identifying miRNA-Disease Associations Using Enhanced Simple Graph Convolutional Networks
EBSD: Short Text Sentiment Classification Using Sentence Vector Enhancement Mechanism
Predicting disease genes based on multi-head attention fusion
Identifying miRNA-Disease Associations Based on Simple Graph Convolution with DropMessage and Jumping Knowledge
Enhancing Protein Subcellular Localization Prediction Through Multi-Feature Fusion
Image-Based Scam Detection Method Using an Attention Capsule Network