Description

Book Synopsis

.- 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.

Advanced Intelligent Computing Technology and Applications

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    A Paperback by De-Shuang Huang

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      View other formats and editions of Advanced Intelligent Computing Technology and Applications by De-Shuang Huang

      Publisher: Springer
      Publication Date: 21/08/2025
      ISBN13: 9789819500291, 978-9819500291
      ISBN10:
      Also in:
      Artificial intelligence (AI)

      Description

      Book Synopsis

      .- 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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