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Bioinformatics research and applications = 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024 : proceedings.. Part I /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bioinformatics research and applications/ edited by Wei Peng, Zhipeng Cai, Pavel Skums.
其他題名:	20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024 : proceedings.
其他題名:	ISBRA 2024
其他作者:	Peng, Wei.
團體作者:	ISBRA (Conference)
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xxii, 511 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Bioinformatics - Congresses. -
電子資源:	https://doi.org/10.1007/978-981-97-5128-0
ISBN:	9789819751280

Bioinformatics research and applications = 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024 : proceedings.. Part I /
Bioinformatics research and applications20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024 : proceedings.Part I /[electronic resource] :ISBRA 2024edited by Wei Peng, Zhipeng Cai, Pavel Skums. - Singapore :Springer Nature Singapore :2024. - xxii, 511 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,149541611-3349 ;. - Lecture notes in computer science ;14954..

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.

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.

ISBN: 9789819751280

Standard No.: 10.1007/978-981-97-5128-0doiSubjects--Topical Terms:

731254
Bioinformatics
--Congresses.

LC Class. No.: QH324.2

Dewey Class. No.: 570.285

Bioinformatics research and applications = 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024 : proceedings.. Part I /
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