Bioinformatics Research and Applications (eBook, PDF)

20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024, Proceedings, Part I
Redaktion: Peng, Wei; Skums, Pavel; Cai, Zhipeng

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 20th International Symposium on Bioinformatics Research and Applications, ISBRA 2024, held in Kunming, China, in July 19-21, 2024.

The 93 full papers included in this book were carefully reviewed and selected from 236 submissions. The symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 511
• Erscheinungstermin: 11. Juli 2024
• Englisch
• ISBN-13: 9789819751280
• Artikelnr.: 72243688

Inhaltsangabe

.- 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 Protein's 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.