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Advanced Intelligent Computing Technology and Applications 21st International Conference, ICIC 2025, Ningbo, China, July 26–29, 2025, Proceedings, Part XXVI /

The 20-volume set LNCS 15842-15861, together with the 4-volume set LNAI 15862-15865 and the 4-volume set LNBI 15866-15869, constitutes the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The 1206 papers pr...

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Bibliografski detalji
Autor kompanije: SpringerLink (Online service)
Daljnji autori: Huang, De-Shuang (Urednik), Chen, Haiming (Urednik), Li, Bo (Urednik), Zhang, Qinhu (Urednik)
Format: Elektronički e-knjiga
Jezik:engleski
Izdano: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Izdanje:1st ed. 2025.
Serija:Lecture Notes in Bioinformatics, 15867
Teme:
Computational intelligence.
Computer networks .
Machine learning.
Application software.
Computational Intelligence.
Computer Communication Networks.
Machine Learning.
Computer and Information Systems Applications.
Online pristup:https://doi.org/10.1007/978-981-95-0030-7
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Sadržaj:
  • -- Cheminformatics.
  • Topological Analysis of F-Multiplicity Corona Graphs: Zagreb Indices and Applications in Molecular Design.
  • Graph-based Multi-scale Learning for Predicting Mass Spectra from Molecules.
  • A Universal Periodicity Injection Module for Crystal Property Prediction.
  • SM-CBNet: A Speech-Based Parkinson's Disease Diagnosis Model with SMOTE–ENN and CNN+BiLSTM Integration.
  • Systems Biology.
  • SpatialDSSC: Estimating Cell Type Abundance and Expression Profile from Spatial Transcriptomic Data.
  • Cuproptosis-related genes are correlated with prognostic and immune infiltration in skin cutaneous melanoma patients.
  • CS-Phylo: Accelerating Evolutionary Distance Estimation with Closed Syncmer-Enhanced MinHash.
  • Aligning Histological Images and Spatial Gene Expression Profiles via Dynamic Convolution and Graph Transformers.
  • SGAEMVN: a hybrid neighborhood-based graph attention autoencoder for identifying spatial domains from spatial transcriptomics.
  • MMF2Drug: A Multi-Modal Feature Fusion Method for Improving Targeted Drug Design.
  • DFDGRU-DTI: Drug-Target Interaction Prediction Based on Random Walk Embeddings and Bidirectional GRU Neural Network.
  • RDT-Net: A Novel Diffusion-Based Network for Intracranial Hemorrhage Segmentation.
  • Feature Attribution-based Explanation Comparison of Magnetoencephalography Decoding Models.
  • scAFC: Adaptive Fusion Clustering of Single-cell RNA-seq Data through Autoencoder and Graph Attention Networks.
  • BIOFUSE-DDI: A Dual-Source Transformer Framework for Drug-Drug Interaction Prediction.
  • MedMaskDiff: Mamba-based Medical Semantic Image Synthesis for Segmentation.
  • Image Clarity Combination Method Based on Hybrid Sampling.
  • PDA-PAGCN: Predicting Disease-Related piRNA Based on Proxy Attention Graph Convolutional Network.
  • CALM-AcPEP: Predicting Anticancer Peptides using Cross-Attention and Pre-trained Language Model.
  • ACP-TransLSTM: A Novel Deep Learning Framework for Anticancer Peptide Prediction Using Multi-Source Feature Integration.
  • Multimodal GAN Integrating Hypergraph and Knowledge Graph Representations for Synthetic Lethality.
  • EdgeViewDet: Dynamic Edge-Centric Fusion Network with Granger Causality for Neurological Disorders Detection.
  • MOMTERL: Modeling Molecular Masking and Contrastive Learning Based on Motifs.
  • Respiratory Sound Classification via Multi-View Feature Fusion with Enhanced Convolutional Neural Network and Audio Spectrogram Transformer.
  • Multi-Scale Graph Regularized Deep Learning for Accurate Drug-Protein Interaction Prediction.
  • DiffiT-HSFDA: Diffusion Based Source-Free Domain Adaptation for Histopathology.
  • Dual-channel MiRNA Drug Resistance Prediction Model Based on Multimodal Feature Alignment.
  • Whole Slide Images Based Cancer Survival Prediction Using Multi-Task Learning.
  • Leveraging DermoGrabcut Segmentation for Improved CNN-Based Skin Lesion Classification.
  • VirB: A Virus Hierarchical Classification Method Based on ModernBERT.
  • FAPE-DTI: Enhancing Drug–Target Interaction Prediction with Focal Attention and Relative Positional Encoding.
  • An Adaptive Multi-View Feature Fusion Framework Based on Multiple Graphs for Predicting Drug-drug Interactions.
  • E-MSNGO: Explainable Multi-species Protein Function Prediction Model based on Aggregated Networks.
  • PRNet: A Contrastive Ranking Model Based on 3D Convolution and Bi LSTM for ChRs Prediction.
  • DeepGO-ESM: Improving the Protein Function Prediction of DeepGraphGO via the Evolutionary Scale Modeling Framework.
  • scGECA: a Graph Embedded Representation Learning Approach with Dynamic Attention Mechanism for Single-cell Clustering.
  • ChemTransGNN++: From Reactants to Products via Multiscale Graph Transformer Modeling of Reaction Pathways.
  • ReAlign-Star: An Optimized Realignment Method for Multiple Sequence Alignment, Targeting Star Algorithm Tools.
  • FMAlign3: A Scalable and Adaptive Framework for Large-Scale Multiple Sequence Alignment.
  • Enough Consecutive Matches in k-Tuple Common Substrings.
  • DeepCatl: A combination of channel attention mechanism and Transformer encoding to predict transcription factor binding sites.
  • FDA-YOLO: Fast Domain Adaptation YOLO for Cross-Domain Brain Tumor Detection in Medical Imaging.
  • Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response Prediction.

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