LEADER 11390nam 2200541 450 001 996485668503316 005 20230112031216.0 010 $a3-031-13829-5 035 $a(MiAaPQ)EBC7073986 035 $a(Au-PeEL)EBL7073986 035 $a(CKB)24547756400041 035 $a(PPN)264191307 035 $a(EXLCZ)9924547756400041 100 $a20230112d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aIntelligent computing theories and application$hPart II $e18th International Conference, ICIC 2022, Xi'an, China, August 7-11, 2022, proceedings /$feditors, De-Shuang Huang [and five others] 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (843 pages) 225 1 $aLecture notes in computer science ;$vVolume 13394 311 08$aPrint version: Huang, De-Shuang Intelligent Computing Theories and Application Cham : Springer International Publishing AG,c2022 9783031138287 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Contents - Part II -- Biomedical Data Modeling and Mining -- A Comparison Study of Predicting lncRNA-Protein Interactions via Representative Network Embedding Methods -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Survey of Network Embedding Methods -- 2.3 LncRNA-Protein Interactions Prediction -- 3 Results and Discussion -- 4 Conclusion -- References -- GATSDCD: Prediction of circRNA-Disease Associations Based on Singular Value Decomposition and Graph Attention Network -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Feature Representation -- 2.3 Singular Value Decomposition for Feature Noise Reduction -- 2.4 Graph Attention Network Embedding Features -- 2.5 Neural Network for Prediction -- 2.6 Evaluation Criteria -- 3 Experiments and Results -- 3.1 GATSDCD Performance -- 3.2 Impact of Parameters -- 3.3 Ablation Study -- 3.4 Performance Comparison with Other Methods -- 3.5 Case Study -- 4 Conclusion -- References -- Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset and Data Processing -- 3.2 Hierarchical Clustering -- 3.3 Model Building -- 3.4 Multiple Stepwise Regression -- 3.5 Fisher Discrimination -- 4 Experimental Results -- 4.1 MLP Results -- 4.2 Results of Stepwise Regression -- 4.3 Optimization of Candidate Compounds Based on Fisher Discriminant -- 5 Conclusion -- References -- Real-Time Optimal Scheduling of Large-Scale Electric Vehicles Based on Non-cooperative Game -- 1 Introduction -- 2 Mathematical Models of New Energy Microgrid and Electric Vehicle Charging and Discharging Behavior -- 2.1 The Price Function of Selling Electricity of New Energy Microgrid -- 2.2 Modeling of Electric Vehicle Charging and Discharging Behavior -- 3 Optimization Objective. 327 $a4 Decentralized Electric Vehicle Control Method Based on Non-cooperative Game -- 4.1 Non-cooperative Game Model -- 4.2 Broadcast Programming for Strategy Solving -- 5 Experimental Results -- 5.1 Evaluation Index -- 5.2 Experimental Results -- 6 Conclusion -- References -- TBC-Unet: U-net with Three-Branch Convolution for Gliomas MRI Segmentation -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 TBC Module -- 3.2 Loss Function -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Metrics for Evaluation -- 4.3 Experiment Detail -- 4.4 Ablation Study -- 4.5 Results -- 5 Conclusion -- References -- Drug-Target Interaction Prediction Based on Graph Neural Network and Recommendation System -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Attribute Representation -- 2.3 Graph Convolutional Network -- 2.4 Neural Factorization Machine -- 2.5 Architecture -- 3 Result and Discussion -- 3.1 Evaluation Criteria -- 3.2 Performance Evaluation of GCNNFM Using 5-Fold Cross-Validation -- 3.3 Compared GCNNFM with Different Machine Learning Algorithms -- 3.4 Compared GCNNFM with Existing State-of-the-Art Prediction Methods -- 4 Conclusions -- References -- NSAP: A Neighborhood Subgraph Aggregation Method for Drug-Disease Association Prediction -- 1 Introduction -- 2 Dataset -- 3 Method -- 3.1 Neighborhood Graph Extraction -- 3.2 Metagraph and Contextual Graph Extraction -- 3.3 Metagraph and Contextual Graph Aggregation -- 3.4 Link Prediction -- 4 Experiment -- 4.1 Comparison Methods -- 4.2 Comparison of Results -- 4.3 Parameter Sensitivity Analysis -- 5 Conclusion -- References -- Comprehensive Evaluation of BERT Model for DNA-Language for Prediction of DNA Sequence Binding Specificities in Fine-Tuning Phase -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Model Architectures -- 2.3 Training and Fine-Tuning. 327 $a3 Results and Analysis -- 3.1 Relatively Small Learning Rate Leads to Better Performance -- 3.2 DNABERT with Different k Value of k-mer Embedding Achieves Similar Performances -- 3.3 DNABERT Achieves Outstanding Performance Overall -- 4 Conclusion -- References -- Identification and Evaluation of Key Biomarkers of Acute Myocardial Infarction by Machine Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Collection -- 2.2 DEG Screening -- 2.3 GO, KEGG, DO and GSEA Enrichment Analysis -- 2.4 Screening and Identification of Gene Prediction Model for Early Diagnosis -- 2.5 The Immune Cell Infiltration Analysis -- 3 Results -- 3.1 Preprocessing and Analysis of AMI-Related Differentially Expressed Genes -- 3.2 GO, KEGG, DO and GSEA Enrichment Analysis of Differential Genes -- 3.3 Screening and Identification of Gene Prediction Model for Early Diagnosis -- 3.4 Immune Infiltration Analyses -- 4 Discussion -- References -- Glioblastoma Subtyping by Immuogenomics -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Collection -- 2.2 Cluster Analysis -- 2.3 Evaluation of Tumor Components -- 2.4 GO, KEGG Pathway and GSEA Analysis -- 2.5 Statistical Methods -- 3 Results -- 3.1 Clinical Information of Patients in the Cancer Genome Atlas Dataset -- 3.2 Immune Typing and Immune Scoring -- 3.3 Correlation Between Immune Typing and Human Leukocyte Antigen, Smoking and Some Immune Genes -- 3.4 Distribution and Gene Enrichment Analysis of Tumor-Infiltrating Immune Cells in Immunophenotyping -- 4 Discussion -- References -- Functional Analysis of Molecular Subtypes with Deep Similarity Learning Model Based on Multi-omics Data -- 1 Introduction -- 2 Methodology -- 2.1 Dataset Collection and Processing -- 2.2 The Proposed Workflow -- 2.3 Performance Evaluation Metrics -- 3 Experimental Results -- 3.1 Performance Validation. 327 $a3.2 Clinical Characteristics Analysis of Ovarian Subtypes -- 3.3 Biological Function Analysis of Breast Molecular Subtypes -- 4 Conclusion and Discussion -- References -- Predicting Drug-Disease Associations by Self-topological Generalized Matrix Factorization with Neighborhood Constraints -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Materials and Preprocessing -- 3.2 Weighted Similarity Data Fusion -- 3.3 NSGMF for DDAs Prediction -- 4 Experiments -- 4.1 Ablation Studies -- 4.2 Comparison with State-of-the-Art DDAs Prediction Methods -- 4.3 Case Studies -- 5 Conclusion -- References -- Intelligent Computing in Computational Biology -- iEnhancer-BERT: A Novel Transfer Learning Architecture Based on DNA-Language Model for Identifying Enhancers and Their Strength -- 1 Introduction -- 2 Materials and Methods -- 2.1 Benchmark Datasets -- 2.2 Methods -- 2.3 Two-Stage Identification Framework -- 2.4 Baseline Method -- 2.5 Performance Evaluation Metrics -- 3 Experimental Results -- 3.1 Different k-mer Pre-training Models -- 3.2 Effect of Pre-training on Model Performance -- 3.3 Effect of Different Fine-Tuning Methods -- 3.4 Performance Comparison with Existing Methods -- 4 Discussion and Conclusion -- References -- GCNMFCDA: A Method Based on Graph Convolutional Network and Matrix Factorization for Predicting circRNA-Disease Associations -- 1 Introduction -- 2 Materials and Methods -- 2.1 Known CircRNA-Disease Association -- 2.2 Disease Semantic Similarity Network -- 2.3 CircRNA Functional Similarity Network -- 2.4 Gaussian Interaction Profile Kernel Similarity for CircRNA and Disease -- 2.5 Combine Multiple Similarity (CircRNA and Disease) -- 2.6 Feature Extraction Based on Graph Convolution Networks -- 2.7 CircRNA-disease Association Prediction and Loss Function -- 3 Results and Discussion -- 3.1 Experimental Setup. 327 $a3.2 Performance Analysis -- 3.3 Compared with Other Methods -- 3.4 Parameters Setting -- 3.5 Case Studies -- 4 Conclusions -- References -- Prediction of MiRNA-Disease Association Based on Higher-Order Graph Convolutional Networks -- 1 Introduction -- 2 Material and Methods -- 2.1 Human MiRNA-disease Associations Database -- 2.2 MiRNA Functional Similarity -- 2.3 Disease Semantic Similarity -- 2.4 Gaussian Interaction Profile Kernel Similarity for MiRNAs and Diseases -- 2.5 Integrated Similarity for MiRNAs and Diseases -- 2.6 MIXHOPMDA -- 3 Results -- 3.1 Experiment Settings -- 3.2 Performance Evaluation -- 3.3 Effect of Number of Projection Dimensions -- 3.4 Effect of Number of Layers -- 3.5 Effect of Number of the Value of P -- 3.6 Comparison with Other Latest Methods -- 4 Case Studies -- 5 Conclusion -- References -- SCDF: A Novel Single-Cell Classification Method Based on Dimension-Reduced Data Fusion -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Normalization -- 2.3 Determining the Optimal Number of Low-Dimensional Components -- 2.4 Concatenation -- 2.5 Classification Using Fused Data -- 3 Result -- 3.1 The Optimal Number of Low-Dimensional Components -- 3.2 The Accuracy of Classification with SCDF -- 4 Conclusion -- References -- Research on the Potential Mechanism of Rhizoma Drynariae in the Treatment of Periodontitis Based on Network Pharmacology -- 1 Introduction -- 2 Material and Method -- 2.1 Screening of the Active Ingredients of Rhizoma Drynariae and Corresponding Targets -- 2.2 Periodontitis Related Targets Retrieval -- 2.3 Common Targets of Rhizoma Drynariae and Periodontitis -- 2.4 Network of Rhizoma Drynariae Active Ingredient and Periodontal Disease Target -- 2.5 Protein-Protein Interaction (PPI) Network -- 2.6 GO and KEGG Pathway Analysis -- 3 Results. 327 $a3.1 Active Compounds and Corresponding Targets in Rhizoma Drynariae. 410 0$aLecture notes in computer science ;$vVolume 13394. 606 $aMachine learning$xIndustrial applications 606 $aComputational intelligence$vCongresses 606 $aBiomedical engineering$xData processing 615 0$aMachine learning$xIndustrial applications. 615 0$aComputational intelligence 615 0$aBiomedical engineering$xData processing. 676 $a006.3 702 $aHuang$b De-Shuang 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996485668503316 996 $aIntelligent Computing Theories and Application$91890195 997 $aUNISA