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Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Juli 2025
Genre:	Naturwissensch., Medizin, Technik
	angewandte informatik / Computational Intelligence / Computer and Information Systems Applications / Computer Communication Networks / Evolutionary Computing and Learning / Image processing / Information Security / machine learning / Maschinelles Lernen / Netzwerk-Hardware / Neural Networks / pattern recognition / Signal Processing / Swarm Intelligence and Optimization mehr...
ISBN:	9789819500291
EAN-Code:	9789819500291
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D
Seiten:	515
Illustration:	XXI, 515 p. 192 illus., 169 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

.- 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 Mul