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[null,null,"Titolo",null,"Advanced Intelligent Computing in Bioinformatics : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part II / / edited by De-Shuang Huang, Yijie Pan, Qinhu Zhang"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"981-9756-92-8"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (505 pages)"]
[null,null,"Collana",null,"Lecture Notes in Bioinformatics, , 2366-6331 ; ; 14882"]
[null,null,"Disciplina",null,"572.80285"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Bioinformatics"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computational and Systems Biology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Biomedical Data Modeling and Mining -- AAHLDMA: Predicting Drug-Microbe Associations Based on Bridge Graph Learning -- 1 Introduction -- 2 Materials -- 2.1 Drug Similarity Attribute -- 2.2 Drug Network Topological Attribute -- 2.3 Fused Drug Attribute -- 2.4 Microbe Functional Similarity Attribute -- 2.5 Microbe Sequence Attribute -- 2.6 Fused Microbe Attribute -- 3 Methods -- 3.1 Attention-Based Graph Autoencoder -- 3.2 Construction of Bridge Graph -- 3.3 Classification -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Model Performance -- 4.3 Performance Comparison with Other Models -- 4.4 Case Study -- 5 Conclusion -- References -- Adaptive Weight Sampling and Graph Transformer Neural Network Framework for Cell Type Annotation of Scrna-seq Data -- 1 Introduction -- 2 Materials -- 2.1 scRNA-seq Datasets -- 2.2 Gene Interaction Networks -- 3 Methods -- 3.1 Adaptive Sampling -- 3.2 Graph Representation Module -- 4 Experimental Results -- 4.1 Model ACC Performance -- 4.2 Model ACC Performance -- 4.3 Sankey Diagram Representation of the Model on the Data Set -- 5 Conclusion -- References -- BiLETCR: An Efficient PMHC-TCR Combined Forecasting Method -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 BiLETCR Model "]
[{"$nulls":4},"Structure and Forecasting Process -- 3.2 EMA -- 3.3 Model Training -- 4 Experiments -- 4.1 Data Collection and Processing -- 4.2 Experimental Design -- 4.3 Adding EMA Module Can Improve the Performance of the Model -- 4.4 For the Generalization Test of BiLETCR, the Prediction Effect of BiLETCR is Better Than the Existing Model -- 4.5 BiLETCR is Superior to the Existing Model in Computational Efficiency -- 4.6 BiLETCR is Superior to the Existing Prediction Tools on Ts-Special Test Set -- 5 Conclusion -- References."]
[{"$nulls":4},"CDDTR: Cross-Domain Autoencoders for Predicting Cell Type Specific Drug-Induced Transcriptional Responses -- 1 Introduction -- 2 Materials and Methods -- 2.1 Within-Domain Reconstruction Paths -- 2.2 Cross-Domain Reconstruction Paths -- 2.3 Training and Prediction Procedures -- 2.4 Comparison with Alternative Methods -- 3 Results -- 3.1 Comparison Results with the State-of-the-Art Methods -- 3.2 The Performance of CDDTR on Small Sample Data -- 3.3 Biological Interpretability of CDDTR Model -- 3.4 Further Improvement of Prediction Performance of CDDTR -- 3.5 Case Study -- 4 Conclusion and Discussion -- References -- ChiMamba: Predicting Chromatin Interactions Based on Mamba -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets and Processing -- 2.2 Selective State Space Models -- 2.3 ChiMamba Model -- 3 Experiment -- 3.1 Datasets and Experiment Setup -- 3.2 Comparative Studies -- 3.3 Ablation Studies -- 3.4 Training Time -- 4 Discussion -- References -- Cluster Analysis of Scrna-Seq Data Combining Bioinformatics with Graph Attention Autoencoders and Ensemble Clustering -- 1 Introduction -- 2 Materials -- 2.1 Dataset -- 2.2 Processing Gene Expression Matrix -- 2.3 Denoising Using Network Enhancement -- 2.4 Performing Principal Component Analysis -- 3 Methods -- 3.1 Graph Attention Autoencoder -- 3.2 Bipartite Graph Ensemble Clustering Method -- 4 Experimental Results -- 4.1 Model Performance -- 4.2 Comparison of Different Model -- 4.3 Comparison of Different Datasets -- 5 Conclusion -- References -- Compound-Protein Interaction Prediction with Sparse Perturbation-Aware Attention -- 1 Introduction -- 2 Methodology -- 2.1 Prediction Backbone -- 2.2 Perturbation-Aware Attention -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Implementation Details -- 3.3 Comparative Performance -- 3.4 Impacts of Modules and Parameters -- 3.5 Case Study."]
[{"$nulls":4},"4 Related Work -- 5 Conclusion -- References -- CUK-Band: A CUDA-Based Multiple Genomic Sequence Alignment on GPU -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The Strategy of Affine Gap Penalty and K-band -- 3.2 Improved Central Star Strategy Based on Bitmap -- 3.3 The K-band Strategy Based on CUDA -- 4 Performance Evaluation -- 4.1 Datasets and Evaluation Criterion -- 4.2 Configuration -- 4.3 Results -- 5 Conclusion -- References -- DeepMHAttGRU-DTI: Prediction of Drug-Target Interactions Based on Knowledge Graph Random Walk Embeddings and GRU Neural Network -- 1 Introduction -- 2 Materials and Methods -- 2.1 Graph Embedding Algorithm Based on Three Improved Random Walk Algorithms -- 2.2 GRU Binary Classification Neural Network Model -- 2.3 Multi-head Attention -- 3 Experimental Results -- 3.1 Evaluation Criteria -- 3.2 General Dataset -- 3.3 Comparison of Different Random Walk Algorithms Using GRU Model -- 3.4 Comparison Between the GRU Model and the MHAttGRU Model -- 3.5 Comparison with Other Existing Models -- 4 Conclusion -- References -- DiagNCF: Diagnosis Neural Collaborative Filtering for Accurate Medical Recommendation -- 1 Introduction -- 2 Preliminaries -- 2.1 Setup and Notation -- 2.2 Data-Preprocessing -- 3 Diagnose Neural Collaborative Filtering (DiagNCF) -- 3.1 General Framework -- 3.2 Generalized Matrix Factorization "]
[{"$nulls":4},"(GMF) -- 3.3 Multi-Layer Perception (MLP) -- 3.4 DiagNCF -- 4 Experiments -- 4.1 Performance Evaluation -- 4.2 Training Procedure -- 5 Conclusions -- References -- Drug Molecule Generation Method Based on Fusion of Protein Sequence Features -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 Targeted Drug Generation Process -- 3 Experimental Results -- 3.1 Evaluation of Model Performance -- 3.2 Molecular Docking Results -- 4 Conclusion -- References."]
[{"$nulls":4},"Drug Target Affinity Prediction Based on Graph Structural Enhancement and Multi-scale Topological Feature Fusion -- 1 Introduction -- 2 Methods -- 2.1 Model Architecture -- 2.2 Drug Feature Extraction Module -- 2.3 Protein Feature Extraction Module -- 2.4 Multi-scale Topological Feature Fusion Module -- 3 Results and Discussion -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Parameters Setting -- 3.4 Performance Comparison with Baseline Model -- 4 Ablation Experiment -- 5 Conclusion -- References -- Drug-Target Interaction Prediction Based on Multi-path Graph Convolution and Graph-Level Attention Mechanism -- 1 Introduction -- 2 Methods -- 2.1 Method Overview -- 2.2 Feature Extraction -- 2.3 Multi-feature Graph Convolution Module -- 2.4 Loss Function -- 3 Results -- 3.1 Dataset -- 3.2 Experiment Settings -- 3.3 Comparisons with Other Baseline Methods -- 3.4 Ablation Experiments -- 3.5 Model Generalization Test -- 4 Conclusion -- References -- Fully Convolutional Neural Network for Predicting Cancer-Specific CircRNA-MiRNA Interaction Sites -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Modelling -- 3 Results -- 4 Discussion and Conclusion -- References -- GSDPI: An Integrated Feature Extraction Framework for Predicting Novel Drug-Protein Interaction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Low-Dimensional Feature Vectors and Feature Similarity Matrices -- 2.3 Determining the Dimensionality of Feature Matrix -- 2.4 Calculation of the Feature Similarity Matrices -- 2.5 DPI Prediction Model Based on GSDPI -- 3 Experimental Evaluation -- 3.1 Evaluation Metrics -- 3.2 Method Comparison and Parameter Settings -- 3.3 Experimental Comparison -- 3.4 Ablation Experiments -- 3.5 Integrate the Gene Ontology (GO) Annotation for All Drug Target-Coding Genes -- 3.6 Case Study -- 4 Conclusion -- References."]
[{"$nulls":4},"Heterogeneous Genome Compression on Mobile Devices -- 1 Introduction -- 2 Related Works -- 2.1 Genome Data Compression -- 2.2 Hardware Accelerated Bioinformatics -- 3 Background -- 3.1 Heterogeneity of MPSoC -- 3.2 Dynamic Voltage-Frequency Scaling -- 4 Methods -- 4.1 Distribute Tasks Transparently -- 4.2 Pipeline Organization -- 5 Results and Discussions -- 5.1 Test Data -- 5.2 Performance and Energy Efficiency Improvements of Heterogeneous Gzip -- 5.3 Exploration for the Reason of Extra Energy Consumption and Discussion -- 6 Conclusion -- References -- HyperCPI: A Novel Method Based on Hypergraph for Compound Protein Interaction Prediction with Good Generalization Ability -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Hypergraphs -- 2.3 Model Architecture of HyperCPI -- 3 Results and Discussion -- 3.1 Performance on OOD Experiments -- 3.2 Ablation Study -- 4 Conclusion -- References -- iEMNN: An Iterative Integration Method for Single-Cell Transcriptomic Data Based on Network Similarity Enhancement and Mutual Nearest Neighbors -- 1 Introduction -- 2 Materials and Methods -- 2.1 Overview of iEMNN -- 2.2 Network Similarity Enhancement -- 2.3 Methods for Comparison -- 2.4 Performance Metrics -- 3 Results -- 3.1 iEMNN Enhances the Similarity of Similar Cells While Separating Distinct Cells -- 3.2 Scenario 1: iEMNN in the Scenario of Identical Cell Types -- 3.3 Scenario 2: iEMNN in the Scenario of Non-identical Cell Types -- 3.4 Scenario 3: iEMNN "]
[{"$nulls":4},"in the Scenario of Multiple Batches -- 3.5 Scenario 4: iEMNN in the Scenario of Cross-Species -- 4 Discussion -- References -- IGDACA: Imaging Genomics of Deep Autoencoder Cascade Attention Fusion Networks for Cervical Cancer Prognosis Prediction -- 1 Introduction -- 2 Method -- 2.1 Model Design -- 2.2 Image Feature Extraction -- 2.3 Gene Feature Extraction -- 2.4 Attention Fusion Module."]
[{"$nulls":4},"3 Experiments and Analyses."]
[null,null,null,"Sommario/riassunto","This two-volume set LNBI 14881-14882 constitutes - in conjunction with the 13-volume set LNCS 14862-14874 and the 6-volume set LNAI 14875-14880 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024. The total of 863 regular papers were carefully reviewed and selected from 2189 submissions. The intelligent computing annual conference primarily aims to promote research, development and application of advanced intelligent computing techniques by providing a vibrant and effective forum across a variety of disciplines. This conference has a further aim of increasing the awareness of industry of advanced intelligent computing techniques and the economic benefits that can be gained by implementing them. The intelligent computing technology includes a range of techniques such as Artificial Intelligence, Pattern Recognition, Evolutionary Computing, Informatics Theories and Applications, Computational Neuroscience & Bioscience, Soft Computing, Human Computer Interface Issues, etc. ."]