Advanced Intelligent Computing Technology and Applications

.- Biomedical Data Modeling and Mining.
.- IMVSC: An Improved Multiview Subspace Clustering in Multimodal 
Medical Image Application.
.- Pathway Variational Auto Encoder for Survival Prediction.
.- Predicting MiRNA-Disease Associations Using Chebyshev Graph  Convolution and Graph.
.- A Hybrid Architecture for 3D Abdominal Medical Images Based on Mamba.
.- MoRE: Structured Multisignal Encoding for Human Disposition Recognition  from Short Media Clips.
.- FPLRDGraph-DTA: Fusing Prior Features and Long-Range Dependent  Sequence Features for Drug-Target Affinity Prediction.
.- A Dual-Loss-GCN Model for Cuffless Blood Pressure Estimation Using  Photoplethysmography.
.- Graph Attention Network and Dynamic Adjustment Mechanism for Drug  Recommendation. 
.- Distilling Closed-Source LLM's Knowledge for Locally Stable and 
Economic Biomedical Entity Linking.
.- Anatomy-aware Mixture of Experts for Medical Vision-Language Pre-training.
.- SC-AGR: Spatially-Constrained Attention for Context-Aware Graph  Representation in Histopathology Whole Slide Image Analysis.
.- MPD-MFF: A Multimodal Parkinson's Disease Detection Method Based on Multi-Feature Fusion.
.- An Efficient Metadata Processing Method Based on Attention Mechanism.
.- MKDTI: Predicting Drug-target Interactions Via Multiple Kernel Fusion on  Graph Attention Network.
.- BiGAMR-Net: Bidirectional Gated Attention and Multi-scale Residual  Network for Polyp Segmentation.
.- HERMES: Heterogeneous Mixture of Experts Based on Segments for  Auditory Attention Decoding.
.- Advanced Predictive Analytics for Hemorrhagic Complications: A Multi Modal Contrastive Learning and Stacking Ensemble Approach.
.- A Prediction Method for Adult Height of Children Based on ACPSO-SVR.
.- Drug–Target Binding Affinity Prediction Based on an Improved Kolmogorov–Arnold Network and Pretrained Models.
.- Landviewer: Characterization of Tissue Landscapes with Multi-view Graph  Learning from Spatially Resolved Transcriptomics.
.- Inter-Relationship Between Pain and Depressive Symptoms in Chinese Middle-Aged and Older People: A Network Analysis.
.- SR-Net: High-Precision Hippocampal Segmentation and Radiomics-Based  Pipeline for Alzheimer's Disease Diagnosis and Prediction.
.- HNGF-NET: Hybrid Neural-Gabor Fusion Network for Brain Glioma  Segmentation.
.- CorGPT: Coronary Angiography Imaging Analysis Using Large Medical  Vision-Language Models.
.- M3Diff: Semantic Mask-Guided 3D Medical Image Synthesis via Mamba-U Net Hybrid for Data Augmentation.
.- KSIR-MIL: Key Region Selection and Instance Refinement for Multi Instance Learning in Whole Slide Image Classification.
.- A Medical Image Segmentation Network for Low-Resource Scenario.
.- OCTAMLLA-UNet: Leveraging Multi-Scale Linear Local Attention for  Accurate OCTA Retinal Image Segmentation.
.- Mitigating High-Scale Dominance in WSI Classification: A Cross-Attention and Hard Instance Mining Framework.
.- Drug-Target Interaction prediction based on lightweight MoE.
.- PLHGMDA: Pre-trained Language model and Heterogeneous Graph neural  network for MiRNA-Disease Association Prediction.
.- DCA-Enhancer: A Dual-Scale Convolutional Attention Network for  Accurate Enhancer Identification and Strength Prediction.
.- Predicting Antibiotic Resistance Genes Using a Hybrid Dataset with NT  Model and BLAST Validation.
.- Masked Bi-LSTM with Unsupervised Encoding for Genomic Breeding Value  Estimation.
.- Intelligent Computing in Drug Design.
.-  Generating a Trustworthy Hypergraph for Traditional Chinese Medicine  Prescription Evaluation and Screening.
.- DrugGAN-MSM: A Generative Adversarial Approach to Molecular Design Integrating Masked Modeling and Multi-Objective Optimization.
.- CroMamba-DTA: Cross-Mamba for Drug-Target Binding Affinity Prediction.
.- CGLDM: A Conditional Geometric Latent Diffusion Model for 3D  Molecular Generation.
.- MetaGT-HGN: A Heterogeneous Graph Neural Network Based on Meta-Learning and a Graph Transformer for Drug Repurposing.
.- Single Cell Spatial Transcriptome.
.- Spatial Transcriptomics Domain Identification Algorithm Based on Multi Scale Contrastive Learning.
.- Low-Rank Multiple Kernel Model based on Local Structures Learning and  Adaptive Similarity Preserving for scRNA-seq Data Clustering.
.- scMGCC: A Self-Supervised Multi-Level Graph Contrastive Learning  Method for scRNA-seq Data Clustering.
.- SMTFusion: Multi-Order Topological Cell Graphs for Single-Cell Multi-Omics Clustering.