E-grāmata: Bioinformatics Research and Applications: 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024, Proceedings, Part I

.- Predicting Drug-Target Affinity Using Protein Pocket and Graph
Convolution Network.



.- MSMK: Multiscale module kernel for identifying disease-related genes.



.- Flat and Nested Protein Name Recognition Based on BioBERT and Biaffine
Decoder.



.- RFIR: A Lightweight Network for Retinal Fundus Image Restoration.



.- Gaussian Beltrami-Klein Model for Protein Sequence Classification: A
Hyperbolic Approach.



.- stEnTrans: Transformer-based deep learning for spatial transcriptomics
enhancement.



.- Contrastive Masked Graph Autoencoders for Spatial Transcriptomics Data
Analysis.



.- Spatial gene expression prediction from histology images with STco.



.- Exploration and Visualization Methods for Chromatin Interaction Data.



.- A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal
Representation.



.- UFGOT: unbalanced filter graph alignment with optimal transport for cancer
subtyping based on multi-omics data.



.- Dendritic SE-ResNet Learning for Bioinformatic Classification.



.- GSDRP: Fusing Drug Sequence Features with Graph Features to Predict Drug
Response.



.- CircMAN: Multi-channel Attention Networks Based on Feature Fusion for
CircRNA-binding Site Prediction.



.- Machine Learning-Driven Discovery of Quadruple-Negative Breast Cancer
Subtypes from Gene Expression Data.



.- A novel Combined Embedding Model based on Heterogeneous Network for
Inferring Microbe-Metabolite Interactions.



.- Central Feature Network Enables Accurate Detection of Both Small and Large
Particles in Cryo-Electron Tomography.



.- LncRNA-disease association prediction based on integrated application of
matrix decomposition and graph contrastive learning.



.- Predictive Score-Guided Mixup for Medical Text Classification.



.- CHASOS: A novel deep learning approach for chromatin loop predictions.



.- A deep metric learning based method for predicting miRNA-disease
associations.



.- Learning an adaptive self-expressive fusion model for multi-omics cancer
subtype prediction.



.- IFNet: An Image-Enhanced Cross-Modal Fusion  Network for Radiology Report
Generation.



.- Hybrid Attention Knowledge Fusion Network for Automated Medical Code
Assignment.



.- Variable-length Promoter Strength Prediction based on Graph Convolution.



.- scMOGAE: A Graph Convolutional Autoencoder-Based Multi-omics Data
Integration Framework for Single-Cell Clustering.



.- VM-UNET-V2: Rethinking Vision Mamba UNet for Medical Image Segmentation.



.- Fighting Fire with Fire: Medical AI Models Defend Against Backdoor Attacks
via Self-Learning.



.- An In-depth Assessment of Sequence Clustering softares in Bioinformatics.



.- Novel Fine-tuning Strategy on Pre-trained Protein Model Enhances ACP
functional Type Classfication.



.- Enhancing Privacy and Preserving Accuracy in Medical Image Classification
with Limited Labeled Samples.



.- gaBERT: an Interpretable Pretrained Deep Learning  Framework for Cancer
Gene Marker Discovery.



.- Hybrid CNN and Low-Complexity Transformer Network with Attention-based
Feature Fusion for Predicting Lung Cancer Tumor after Neoadjuvant
Chemoimmunotherapy.



.- Deep Hyper-Laplacian Regularized Self-Representation Learning based
Structured Association Analysis for Brain Imaging Genetics.



.- IntroGRN: Gene Regulatory Network Inference from single-cell RNA Data
Based on Introspective VAE.



.- Identification of Potential SARS-CoV-2 Main Protease Inhibitors Using Drug
Repurposing and Molecular Modeling.



.- An Ensemble Learning Model for Predicting Unseen TCR-Epitope
Interactions.



.- Deep Learning Approach to Identify Proteins Secondary Structure
Elements.



.- Modeling single-cell ATAC- seq data based on contrastive learning.



.- Continuous Identification of Sepsis-Associated Acute Heart Failure
Patients: An Integrated LSTM-Based Algorithm.



.- A novel approach for subtype identification via multi-omics data using
adversarial autoencoder.