Bioinformatics Research and Applications : 21st International Symposium, ISBRA 2025, Helsinki, Finland, August 3–5, 2025, Proceedings, Part I / edited by Jing Tang, Xin Lai, Zhipeng Cai, Wei Peng, Yanjie Wei.

Format:

Book

Contributor:

Tang, Jing, Editor.

Lai, Xin, Editor.

Cai, Zhipeng, Editor.

Peng, Wei, Editor.

Wei, Yanjie, Editor.

Series:

Lecture Notes in Bioinformatics, 2366-6331 ; 15756

Language:

English

Subjects (All):

Bioinformatics.

Artificial intelligence.

Computer engineering.

Computer networks.

Artificial Intelligence.

Computer Engineering and Networks.

Local Subjects:

Bioinformatics.

Artificial Intelligence.

Computer Engineering and Networks.

Physical Description:

1 online resource (XX, 412 p. 124 illus., 120 illus. in color.)

Edition:

1st ed. 2026.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2026.

Summary:

This two-set volume LNCS 15756 and 15767 constitutes the refereed proceedings of the 21st International Symposium on Bioinformatics Research and Applications, ISBRA 2025, held in Helsinki, Finland, during August 3–5, 2025. The 66 full papers were carefully reviewed and selected from 167 submissions. This year’s symposium brought together leading researchers, scientists, and industry professionals from around the world to share cutting-edge advancements, foster collaboration, and explore the future of bioinformatics and computational biology. .

Contents:

HCSeer: A Classification Tool for Human Genetic Variant Hot and Cold Spots Designed for PM1 and Benign Criteria in the ACMG Guideline

ViDSG: A Hybrid Algorithm Integrating Statistical and Semantic Features via Dual-Channels for Identifying Prokaryotic and Eukaryotic Viruses

MoGE: A Benchmark for Comprehensive Evaluation of Molecular Generation Models in De Novo Drug Design

Dual-Modality Representation Learning for Molecular Property Prediction

GDMRMD: An Ensemble Model for Predicting RNA Modification-Disease Associations

SUIFS: A Symmetric Uncertainty based Interactive Feature Selection Method

TF-GCNNovo: A Peptide Sequence Prediction Model Integrating Transformer and Graph Convolutional Network

FSPicker: A Dual-Stream Attention Network for Multi-Scale Particle Picking in Cryo-Electron Tomography

SDMFF: Spatial-temporal Dual-pathway Network with Multi-scale Feature Fusion for Parkinson’s Disease Diagnosis

RNA-ModCaller: A Multi Feature Fusion and Stacking Ensemble Learning Framework for Prediction of RNA Modifications

Efficient and Accurate Approximation Algorithms for Protein Structure Alignment

Multi-Task Learning with Cross-Stitch for Synergistic Effect of Drug Combination Prediction

A Neighborhood Selection Learning Artificial Bee Colony Algorithm Based on Population Backtracking for Detecting Epistatic Interactions

PDA-GTGCN: identification of piRNA-disease associations based on group feature transformation graph convolutional network

DDLB: Using the protein language model and hierarchical architecture to improve disordered lipid-binding residues prediction

EEG-TFNet: Spatiotemporal and Spectral Feature Integration for EEG-Based AD Detection

RGMI: a multimodal graph framework with dynamic weighting for measuring disease similarity

LDADW: An algorithm for integrating single-cell and spatial transcriptomic data based on the topic model

Adaptive Fusion of Global and Local Representations for Neoantigen Retention Time Prediction through Hierarchical Sequence-Graph Hybridization

MambaST: Hexagonal State Space Modeling for Spatial Domain Identification

On Multiple Protein Scaffold Filling

RGNCNDDA: Predicting Potential Drug-Disease Associations via Residual Graph Normalized Convolutional Network

Spindle-UMamba: A Mamba-based Attention-Unet Framework for Effective Sleep Spindle Detection

CADS: Causal Inference for Dissecting Essential Genes to Predict Drug Synergy

A Novel Sample Selection for Deep Learning Model in Computational Drug Repositioning

SGMDTI: A unified framework for drug-target interaction prediction by semantic-guided meta-path method

TREPP: Tandem Repeat Expansion Pathogenicity Predicting Approach Using Stacked CatBoost Models and Multiple Features

EMF: Enhancing Mortality Risk Prediction via Evidential Multimodal Fusion

Contrastive Learning-based Method for Single-cell Multi-omics Data Clustering

Intelligent algorithms of action recognition for cardiopulmonary resuscitation based on wearable device

Label-guided graph contrastive learning for single-cell fusion clustering

A Graph Convolution-Based Method for dental Image Registration

DepMambaformer: Integrating Bidirectional State Space Duality Model with Multimodal Attention for Depression Detection.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9506-98-0

OCLC:

1530746028