Bioinformatics Research and Applications

.- Exploring Hierarchical Structures of Cell Types in  scRNA-seq Data.
.- Predicting Frequencies of Drug Side Effects Using Graph Attention Networks with Multiple Features.
.- RabbitTrim: highly optimized trimming of Illumina sequencing data on multi-core platforms.
.- A hybrid feature fusion network for predicting HER2 status on H&E-stained histopathology images.
.- scCoRR: a data-driven self-correction framework for labeled scRNA-seq data.
.- KT-AMP: Enhancing Antimicrobial Peptide Functions Prediction through Knowledge Transfer on Protein Language Model.
.- A Multi-Scale Attention Network for Sleep Arousal Detection with Single-Channel ECG.
.- RabbitSAlign: Accelerating Short-Read Alignment for CPU-GPU Heterogeneous Platforms.
.- FedKD-DTI: Drug-Target Interaction Prediction Based on Federated Knowledge Distillation.
.- Accurately Deciphering Novel Cell Type in Spatially Resolved Single-Cell Data through Optimal Transport.
.- Synthesis of Boolean Networks with Weak and Strong Regulators.
.- Patch-based coupled attention network to predict MSI status in colon cancer.
.- Predicting Blood-Brain Barrier Permeability through Multi-View Graph Neural Network with Global-Attention and Pre-trained Transformer.
.- LLMDTA: Improving Cold-Start Prediction in Drug-Target Affinity with Biological LLM.
.- DMSDR: Drug Molecule Synergy-Enhanced Network for Drug Recommendation with Multi-Source Domain Knowledge.
.- A Graph Transformer-Based Method for Predicting LncRNA-Disease Associations Using Matrix Factorization and Automatic Meta-Path Generation.
.- The Dynamic Spatiotemporal Features Based on Rich Club Organization in Autism Spectrum Disorder.
.- Integrated Analysis of Autophagy-Related Genes Identifies Diagnostic Biomarkers and Immune Correlates in Preeclampsia.
.- Multi-Grained Cross-Modal Feature Fusion Network for Diagnosis Prediction.
.- MOL-MOE Learning Drug Molecular Characterization Based on Mixture of Expert Mechanism.
.- A Multimodal Federated Learning Framework for Modality Incomplete Scenarios in Healthcare.
.- FunBGC: An Intelligent Framework for Fungal Biosynthetic Gene Cluster Identification.
.- An Automatic Recommendation Method  for Single-Cell DNA Variant Callers  Based on Meta-Learning Framework.
.- Incomplete Multimodal Learning with Modality-Aware Feature Interaction for Brain Tumor Segmentation.
.- Multi-Scale Mean Teacher for Unsupervised Cross-Modality Abdominal Segmentation with Limited Annotations.
.- Subgraph-aware dynamic attention network for drug repositioning.
.- Multi-filter based signed graph convolutional networks for predicting interactions on drug networks.
.- CPSORCL: A Cooperative Particle Swarm Optimization Method with Random Contrastive Learning for Interactive Feature Selection.
.- Hypergraph representation learning for cancer drug response prediction.
.- DGCL: a contrastive learning method for predicting cancer driver genes based on graph diffusion.
.- KUMA-MI: A 12-Lead Knowledge-guided Multi-branch Attention Networks for Myocardial Infarction Localization.
.- scAHVC: Single-cell Multi-omics clustering algorithm based on adaptive weighted hyper-laplacian regularization.
.- Early Prediction of SGA-LGA Fetus at the First Trimester Ending through Weighted Voting Ensemble Learning Approach.
.- A Hierarchical Classification Model for Annotating Antibacterial Biocide and Metal Resistance Genes via Fusing Global and Local Semantics.
.- Secure Relative Detection in (Forensic) Database with Homomorphic Encryption.
Noninvasive diagnosis of cancer based on the heterogeneity and fragmentation features of cell-free mitochondrial DNA.
.- A Novel Dual Interactive Network for Parkinson's Disease Diagnosis Based on Multi-modality Magnetic Resonance Imaging.
.- DVMPDC: A deep learning model based on dual-view representation and multi-strategy pooling for predicting synergistic drug combinations.
.- MEMDA: a multi-similarity integration pre-completion algorithm with error correction for predicting microbe-drug associations.
.- ResDeepGS:A deep learning-based method for crop phenotype prediction.
.- Benchmarking Biomedical Relation Knowledge in Large Language Models.