{"product_id":"advanced-intelligent-computing-technology-and-applications-9789819500291","title":"Advanced Intelligent Computing Technology and Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e.- Cheminformatics.\u003cbr\u003e\u003c\/strong\u003e.- Topological Analysis of F-Multiplicity Corona Graphs: Zagreb Indices and Applications in Molecular Design.\u003cbr\u003e.- Graph-based Multi-scale Learning for Predicting Mass Spectra from Molecules.\u003cbr\u003e.- A Universal Periodicity Injection Module for Crystal Property Prediction.\u003cbr\u003e.- SM-CBNet: A Speech-Based Parkinson's Disease Diagnosis Model with SMOTE–ENN and CNN+BiLSTM Integration.\u003cbr\u003e\u003cstrong\u003e.- Systems Biology.\u003cbr\u003e\u003c\/strong\u003e.- SpatialDSSC: Estimating Cell Type Abundance and Expression Profile from \u003cbr\u003eSpatial Transcriptomic Data.\u003cbr\u003e.- Cuproptosis-related genes are correlated with prognostic and immune  infiltration in skin cutaneous melanoma patients.\u003cbr\u003e.- CS-Phylo: Accelerating Evolutionary Distance Estimation with Closed Syncmer-Enhanced MinHash.\u003cbr\u003e.- Aligning Histological Images and Spatial Gene Expression Profiles via  Dynamic Convolution and Graph Transformers.\u003cbr\u003e.- SGAEMVN: a hybrid neighborhood-based graph attention autoencoder for  identifying spatial domains from spatial transcriptomics.\u003cbr\u003e.- MMF2Drug: A Multi-Modal Feature Fusion Method for Improving Targeted  Drug Design.\u003cbr\u003e.- DFDGRU-DTI: Drug-Target Interaction Prediction Based on Random Walk Embeddings and Bidirectional GRU Neural Network.\u003cbr\u003e.- RDT-Net: A Novel Diffusion-Based Network for Intracranial Hemorrhage  Segmentation.\u003cbr\u003e.- Feature Attribution-based Explanation Comparison of  Magnetoencephalography Decoding Models.\u003cbr\u003e.- scAFC: Adaptive Fusion Clustering of Single-cell RNA-seq Data through  Autoencoder and Graph Attention Networks.\u003cbr\u003e.- BIOFUSE-DDI: A Dual-Source Transformer Framework for Drug-Drug  Interaction Prediction.\u003cbr\u003e.- MedMaskDiff: Mamba-based Medical Semantic Image Synthesis for  Segmentation.\u003cbr\u003e.- Image Clarity Combination Method Based on Hybrid Sampling.\u003cbr\u003e.- PDA-PAGCN: Predicting Disease-Related piRNA Based on Proxy Attention  Graph Convolutional Network.\u003cbr\u003e.- CALM-AcPEP: Predicting Anticancer Peptides using Cross-Attention and  Pre-trained Language Model.\u003cbr\u003e.- ACP-TransLSTM: A Novel Deep Learning Framework for Anticancer  Peptide Prediction Using Multi-Source Feature Integration.\u003cbr\u003e.- Multimodal GAN Integrating Hypergraph and Knowledge Graph  Representations for Synthetic Lethality.\u003cbr\u003e.- EdgeViewDet: Dynamic Edge-Centric Fusion Network with Granger  Causality for Neurological Disorders Detection.\u003cbr\u003e.- MOMTERL: Modeling Molecular Masking and Contrastive Learning Based  on Motifs.\u003cbr\u003e.- Respiratory Sound Classification via Multi-View Feature Fusion with Enhanced Convolutional Neural Network and Audio Spectrogram  Transformer.\u003cbr\u003e.- Multi-Scale Graph Regularized Deep Learning for Accurate Drug-Protein  Interaction Prediction.\u003cbr\u003e.- DiffiT-HSFDA: Diffusion Based Source-Free Domain Adaptation for Histopathology.\u003cbr\u003e.- Dual-channel MiRNA Drug Resistance Prediction Model Based on  Multimodal Feature Alignment.\u003cbr\u003e.- Whole Slide Images Based Cancer Survival Prediction Using Multi-Task  Learning.\u003cbr\u003e.- Leveraging DermoGrabcut Segmentation for Improved CNN-Based Skin  Lesion Classification.\u003cbr\u003e.- VirB: A Virus Hierarchical Classification Method Based on ModernBERT.\u003cbr\u003e.- FAPE-DTI: Enhancing Drug–Target Interaction Prediction with Focal  Attention and Relative Positional Encoding.\u003cbr\u003e.- An Adaptive Multi-View Feature Fusion Framework Based on Multiple  Graphs for Predicting Drug-drug Interactions.\u003cbr\u003e.- E-MSNGO: Explainable Multi-species Protein Function Prediction Model  based on Aggregated Networks.\u003cbr\u003e.- PRNet: A Contrastive Ranking Model Based on 3D Convolution and Bi LSTM for ChRs Prediction.\u003cbr\u003e.- DeepGO-ESM: Improving the Protein Function Prediction of DeepGraphGO  via the Evolutionary Scale Modeling Framework.\u003cbr\u003e.- scGECA: a Graph Embedded Representation Learning Approach with  Dynamic Attention Mechanism for Single-cell Clustering.\u003cbr\u003e.- ChemTransGNN++: From Reactants to Products via Multiscale Graph Transformer Modeling of Reaction Pathways.\u003cbr\u003e.- ReAlign-Star: An Optimized Realignment Method for Multiple Sequence  Alignment, Targeting Star Algorithm Tools.\u003cbr\u003e.- FMAlign3: A Scalable and Adaptive Framework for Large-Scale Multiple  Sequence Alignment.\u003cbr\u003e.- Enough Consecutive Matches in k-Tuple Common Substrings.\u003cbr\u003e.- DeepCatl: A combination of channel attention mechanism and Transformer  encoding to predict transcription factor binding sites.\u003cbr\u003e.- FDA-YOLO: Fast Domain Adaptation YOLO for Cross-Domain Brain  Tumor Detection in Medical Imaging.\u003cbr\u003e.- Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph  Neural Networks for Drug Response Prediction.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53212848193879,"sku":"9789819500291","price":66.49,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/advanced-intelligent-computing-technology-and-applications-9789819500291","provider":"Book Curl","version":"1.0","type":"link"}