Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Bioinformatics Research and Applications : 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024, Proceedings, Part I
| Bioinformatics Research and Applications : 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024, Proceedings, Part I |
| Autore | Peng Wei |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (531 pages) |
| Altri autori (Persone) |
CaiZhipeng
SkumsPavel |
| Collana | Lecture Notes in Computer Science Series |
| ISBN |
9789819751280
9789819751273 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Predicting Drug-Target Affinity Using Protein Pocket and Graph Convolution Network -- 1 Introduction -- 2 Materials and Methods -- 2.1 Representation of Protein Pocket -- 2.2 Representation of Molecule Structure -- 2.3 Model Architecture -- 2.4 Experimental Setup -- 2.5 Datasets -- 3 Results and Discussion -- 3.1 Evaluation Metrics -- 3.2 Comparison with Other Methods -- 3.3 Ablation Experiments -- 4 Conclusion -- References -- MSMK: Multiscale Module Kernel for Identifying Disease-Related Genes -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Multiscale Module Profile -- 3.2 Multiscale Module Kernel -- 3.3 Methods Integrating Multiscale Module Kernel -- 4 Experimental Results -- 4.1 Experimental Settings -- 4.2 Performance Analysis of Different Fusion Strategies -- 4.3 Performance Analysis of Different Kernel Sparseness -- 4.4 Performance Comparison of Different Algorithms -- 5 Conclusion -- References -- Flat and Nested Protein Name Recognition Based on BioBERT and Biaffine Decoder -- 1 Introduction -- 2 Related Work -- 2.1 Flat Protein Name Recognition -- 2.2 Nested Protein Name Recognition -- 3 Method -- 3.1 Overall Architecture -- 3.2 BioBERT Encoder -- 3.3 Biaffine Decoder -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Results -- 5 Discussion -- 5.1 Ablation Study -- 5.2 Impact of Smoothing Strategy -- 5.3 Visualization Example -- 5.4 Categorical Performances -- 6 Conclusions -- References -- RFIR: A Lightweight Network for Retinal Fundus Image Restoration -- 1 Introduction -- 2 Method -- 2.1 Dynamic Multi-head Self-Attention -- 2.2 Sparse Spatial Self-attention -- 2.3 Feed-Forward Network -- 3 Experiments -- 3.1 Datasets and Implementation Details -- 3.2 Ablation Study -- 3.3 Comparative Experiments.
4 Conclusion -- A High-Resolution Figures -- References -- Gaussian Beltrami-Klein Model for Protein Sequence Classification: A Hyperbolic Approach -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Beltrami-Klein Model -- 3.2 Kernel Matrix from Beltrami-Klein Distance -- 4 Results and Discussion -- 5 Conclusion -- References -- stEnTrans: Transformer-Based Deep Learning for Spatial Transcriptomics Enhancement -- 1 Introduction -- 2 Methods -- 2.1 Data Pre-processing -- 2.2 Self-supervised Learning -- 2.3 Details of StEnTrans -- 3 Experimental Results -- 3.1 StEnTrans Imputes the Gene Expression Accurately -- 3.2 StEnTrans Better Help to Discover Spatial Patterns -- 3.3 Ablation Study -- 4 Conclusions -- References -- Contrastive Masked Graph Autoencoders for Spatial Transcriptomics Data Analysis -- 1 Introduction -- 2 Methods -- 2.1 Data Preprocessing and Augmentation -- 2.2 GCNs Encoder and Decoder -- 2.3 Training Objective -- 2.4 Evaluation Criteria -- 3 Experimental Results -- 3.1 Experimental Datasets -- 3.2 Improved Spatial Domain Recognition Performance -- 3.3 Ablation Study -- 4 Conclusions -- References -- Spatial Gene Expression Prediction from Histology Images with STco -- 1 Introduction -- 2 Materials and Methods -- 2.1 Experimental Datasets -- 2.2 Data Pre-processing -- 2.3 Methods -- 3 Experimental Results -- 3.1 Evaluation Criteria -- 3.2 Comparison with Other Methods -- 3.3 Visualization of the Predicted Gene Expression -- 3.4 Spatial Region Detection -- 3.5 Ablation Study of the Proposed STco Model -- 4 Conclusions -- References -- Exploration and Visualization Methods for Chromatin Interaction Data -- 1 Introduction -- 2 Chromatin Interaction Data -- 2.1 Biological Interpretation -- 2.2 Formal Representation of Interaction Data Sets -- 2.3 Data Sets Used -- 3 Chromatin Data Visualization. 3.1 Data Visualization Module ``Component Visualization'' -- 3.2 Data Visualization Module ``BioClique'' -- 4 Using Data Visualization Tools for Deriving and Verification of Biological Hypotheses -- 5 Conclusions -- References -- A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation -- 1 Introduction -- 2 Method -- 2.1 Graph Construction -- 2.2 SDF Computation -- 2.3 Voxel Hashing and Mesh Extraction -- 3 Experimental Results -- 3.1 Datasets and Experiment Settings -- 3.2 Evaluation Metrics -- 3.3 Qualitative and Quantitative Analysis -- 4 Conclusion and Future Work -- References -- UFGOT: Unbalanced Filter Graph Alignment with Optimal Transport for Cancer Subtyping Based on Multi-omics Data -- 1 Introduction -- 2 Materials and Methods -- 2.1 fGOT -- 2.2 UFGOT -- 2.3 Optimization of UFGOT -- 2.4 Datasets -- 2.5 Benchmarking -- 3 Experimental Results -- 3.1 Selection of Filtering Operators -- 3.2 Combination of Omics Data -- 3.3 Alignment Performance of UFGOT -- 3.4 Clustering Performance of UFGOT -- 4 Discussion -- References -- Dendritic SE-ResNet Learning for Bioinformatic Classification -- 1 Introduction -- 2 Related Work -- 2.1 SE-ResNet -- 2.2 Dendritic Learning -- 3 Methodology -- 3.1 Squeeze-and-Excitation Structure -- 3.2 Dendritic Learning Module -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Model Hyper-parameters Setting -- 4.3 Evaluation Metrics -- 4.4 Result and Discussion -- 5 Conclusion -- References -- GSDRP: Fusing Drug Sequence Features with Graph Features to Predict Drug Response -- 1 Introduction -- 2 Methods -- 3 Results -- 3.1 Experimental Settings and Model Evaluation -- 3.2 Results of Single-Omics and Multi-Omics Comparison Experiments -- 3.3 Performance Comparison of Our Method and Existing Methods -- 3.4 Ablation Study. 3.5 Performance Comparison of Our Method for Predicting Different Cancers -- 3.6 Blind Drugs/Cell-Lines Test -- 3.7 Case Study -- 4 Conclusion and Discussion -- References -- CircMAN: Multi-channel Attention Networks Based on Feature Fusion for CircRNA-Binding Protein Site Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Benchmark Dataset -- 2.2 Feature Encoding Scheme -- 2.3 Deep Neural Network Architecture -- 2.4 Performance Evaluation -- 3 Experimental Settings -- 4 Results -- 4.1 Comparison with Other Methods -- 4.2 Ablation Experiment -- 5 Conclusion -- References -- Machine Learning-Driven Discovery of Quadruple-Negative Breast Cancer Subtypes from Gene Expression Data -- 1 Introduction -- 2 Data -- 3 Methods -- 3.1 Data Preparation -- 3.2 Dimensionality Reduction -- 3.3 Clustering Methodology -- 3.4 Analyzing Cluster Features -- 3.5 Assessing Cluster Performance -- 4 Results -- 4.1 Patient Cluster Identification -- 4.2 Analyzing Cluster Features -- 4.3 Initial Cluster Assessment -- 5 Conclusion -- References -- A Novel Combined Embedding Model Based on Heterogeneous Network for Inferring Microbe-Metabolite Interactions -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 The Overall Flow of the Model -- 2.3 Node Embedding -- 2.4 Paired Embedding -- 2.5 Combined Embedding Model -- 3 Results and Discussion -- 3.1 Evaluation Criteria -- 3.2 Comparison of Algorithms -- 3.3 Ablation Study -- 3.4 Hyperparametric Study -- 3.5 Case Study -- 4 Conclusion -- References -- Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography -- 1 Introduction -- 2 Methods -- 2.1 Central Feature Network (CFN) -- 2.2 Gradient Descent Tracing -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Performance Comparison -- 3.3 Benefits of Adding MLP-Mixer. 3.4 Implementation Details -- 4 Discussions and Conclusions -- References -- LncRNA-Disease Association Prediction Based on Integrated Application of Matrix Decomposition and Graph Contrastive Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Collection -- 2.2 The Overall Flow of the Model -- 2.3 Disease Semantic Similarity -- 2.4 Functional Similarity of LncRNA (MiRNA) -- 2.5 Gaussian Interaction Spectral Kernel Similarity of LncRNAs/miRNAs and Diseases -- 2.6 Similarity Matrix Fusion -- 2.7 LncRNA-miRNA-Disease Graph Construction -- 3 Matrix Decomposition -- 3.1 Nonnegative Matrix Factorization and Matrix Reconstruction -- 3.2 Extracting Linear Features of Nodes by Singular Value Decomposition -- 4 Extracting Node Embeddings by Graph Contrastive Learning -- 4.1 Encoder Based on Graph Convolutional Networks -- 4.2 Constructing Global Representation -- 4.3 Constructing Negative Samples Based on Destructor Function -- 4.4 Discriminator -- 5 Experiments -- 5.1 Experiment Settings -- 5.2 Comparison with Other Baseline Methods -- 5.3 Ablation Experiment -- 6 Case Studies -- 7 Conclusion -- References -- Predictive Score-Guided Mixup for Medical Text Classification -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Encoding Layer and Multi-head Scoring Layer -- 3.2 Score Guided Mixup Layer -- 3.3 The Loss Function -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Baseline -- 4.4 Experimental Environment -- 5 Results -- 5.1 Comparison Experiment -- 5.2 Impact of Dimensionality on Model Performance -- 5.3 Ablation Experiment -- 5.4 Case Study -- 6 Conclusions -- References -- CHASOS: A Novel Deep Learning Approach for Chromatin Loop Predictions -- 1 Introduction -- 2 Materials and Methods -- 2.1 The Workflow of the Model -- 2.2 Construction of Anchor Score Prediction Model. 2.3 Construction of OCR Score Prediction Model. |
| Record Nr. | UNINA-9910874677403321 |
Peng Wei
|
||
| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bioinformatics Research and Applications : 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024, Proceedings, Part II
| Bioinformatics Research and Applications : 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024, Proceedings, Part II |
| Autore | Peng Wei |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (515 pages) |
| Altri autori (Persone) |
CaiZhipeng
SkumsPavel |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 981-9751-31-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part II -- Exploring Hierarchical Structures of Cell Types in scRNA-seq Data -- 1 Introduction -- 2 Related Work -- 2.1 Structural Entropy -- 2.2 Shared Nearest Neighbor -- 3 Method -- 3.1 The Framework of scHSD -- 3.2 Graph Construction -- 3.3 Hierarchy Tree Building via Structural Entropy Minimization -- 3.4 Cell Type Identification -- 4 Results and Discussion -- 4.1 Cell Type Hierarchy -- 4.2 Comparative Analysis of Clustering Results -- 4.3 Visualization -- 4.4 Comparative Analysis of Classifying Results -- 5 Conclusion -- References -- Predicting Frequencies of Drug Side Effects Using Graph Attention Networks with Multiple Features -- 1 Introduction -- 2 Materials and Methods -- 2.1 Benchmark Dataset -- 2.2 Drug Profile -- 2.3 Side Effect Profile -- 2.4 MFGAT -- 3 Result and Discussion -- 3.1 Evaluation Metrics -- 3.2 Comparision with Other Models -- 3.3 Ablation Experiments -- 4 Conclusion and Discussion -- References -- RabbitTrim: Highly Optimized Trimming of Illumina Sequencing Data on Multi-core Platforms -- 1 Introduction -- 2 Methods -- 2.1 Efficient I/O Strategy -- 2.2 Memory Reuse -- 2.3 Bitwise Operations -- 2.4 Vectorization -- 3 Results -- 3.1 Datasets and Platforms -- 3.2 Performance Results -- 4 Conclusion -- References -- A Hybrid Feature Fusion Network for Predicting HER2 Status on H& -- E-Stained Histopathology Images -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Overview of the Proposed Model -- 4 Experiments and Results -- 4.1 Evaluation Metrics -- 4.2 Experimental Results -- 4.3 Feature Fusion Weight Selection -- 5 Discussion -- 6 Conclusions -- References -- scCoRR: A Data-Driven Self-correction Framework for Labeled scRNA-Seq Data -- 1 Introduction -- 2 Method -- 2.1 Data Preprocessing and Anchor Cell Identification.
2.2 Cell Representation Learning and Classification Based on a Supervised Contrastive Learning -- 3 Results -- 3.1 Datasets -- 3.2 Validation on the Baron Dataset Revealed a Subset of Ductal Cells Were Corrected to Acinar Cells -- 3.3 Validation on the Mammary Gland Dataset Revealed a Subset of Stromal Was Corrected to Macrophage -- 3.4 Evaluation of Clustering Results Before and After Cell Label Correction -- 4 Discussion -- References -- KT-AMP: Enhancing Antimicrobial Peptide Functions Prediction Through Knowledge Transfer on Protein Language Model -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Overview of the Model Workflow -- 2.3 Pre-trained Protein Language Model -- 2.4 Fine-Tuning Pre-trained Model -- 2.5 MLP Classifier -- 3 Experiments and Results -- 3.1 Evaluation Metrics -- 3.2 Performance Evaluation -- 3.3 Feature Representation Visualization -- 3.4 Ablation Study -- 4 Discussion and Conclusion -- References -- A Multi-scale Attention Network for Sleep Arousal Detection with Single-Channel ECG -- 1 Introduction -- 2 Methods -- 2.1 Data Preprocessing -- 2.2 Model Architecture -- 2.3 Loss Objective -- 2.4 Performance Metrics -- 3 Experiment Results and Discussion -- 3.1 Experimental Setup -- 3.2 Arousal Detection Performance Comparison -- 3.3 Arousal Index Performance Comparison -- 3.4 Ablation Experiments -- 3.5 Visualized Analysis -- 4 Conclusion and Future Work -- References -- RabbitSAlign: Accelerating Short-Read Alignment for CPU-GPU Heterogeneous Platforms -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Seeding Optimization -- 2.3 GPU Acceleration -- 3 Experimental Results -- 3.1 Overview -- 3.2 Efficiency Evaluation -- 3.3 Accuracy Evaluation -- 4 Conclusion -- References -- FedKD-DTI: Drug-Target Interaction Prediction Based on Federated Knowledge Distillation -- 1 Introduction -- 2 Methods. 2.1 Drug-Target Interaction Prediction Model -- 2.2 Overview of FedKD-DTI -- 3 Experiment -- 3.1 Experimental Setups -- 3.2 Results -- 4 Conclusion -- References -- Accurately Deciphering Novel Cell Type in Spatially Resolved Single-Cell Data Through Optimal Transport -- 1 Introduction -- 2 Method -- 2.1 OT-Based Representation Learning for Novel Cell Type Discovery -- 2.2 OT-Based Partial Alignment for Seen Cell Type Identification -- 2.3 Re-weighted Entropy Loss to Increase the Prediction Certainty -- 3 Results and Discussion -- 3.1 Settings -- 3.2 Results -- 3.3 Ablation Study -- 4 Conclusion -- References -- Synthesis of Boolean Networks with Weak and Strong Regulators -- 1 Introduction -- 2 Definitions -- 2.1 Strength of Regulators -- 2.2 Regulation Conditions and Monotonic Definition -- 3 Synthesis Implementation -- 3.1 Variables -- 3.2 Constraints -- 3.3 DEFINE -- 3.4 Linear Temporal Logic Specification - LTLSPEC -- 3.5 Integration -- 4 Results -- 4.1 Application to Toy Example -- 4.2 Application to Mammalian Cell Cycle Modelling -- 5 Related Work -- References -- Patch-Based Coupled Attention Network to Predict MSI Status in Colon Cancer -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Methodology -- 4 Experiments and Results -- 4.1 Experimental Setting -- 4.2 Comparison Experiments -- 4.3 Effects of Attention Mechanisms -- 4.4 Limitations -- 5 Conclusion -- References -- Predicting Blood-Brain Barrier Permeability Through Multi-view Graph Neural Network with Global-Attention and Pre-trained Transformer -- 1 Introduction -- 2 Materials and Methods -- 2.1 Materials -- 2.2 Methods -- 3 Experiments and Results -- 3.1 Experimental Settings and Evaluation Metrics -- 3.2 Comparing with Different Methods in Hold-Out Validation -- 3.3 Comparing with Different Methods in 5-CV -- 4 Conclusion -- References. LLMDTA: Improving Cold-Start Prediction in Drug-Target Affinity with Biological LLM -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Model -- 3 Experiments and Results -- 3.1 Experimental Setup -- 3.2 Experimental Results -- 3.3 Ablation Study -- 3.4 Case Study -- 4 Conclusion -- References -- DMSDR: Drug Molecule Synergy-Enhanced Network for Drug Recommendation with Multi-source Domain Knowledge -- 1 Introduction -- 2 Methods -- 2.1 Patient Representation Module -- 2.2 The Drug Molecular Synergy Module -- 2.3 Domain Knowledge Representation Module -- 2.4 Drug Prediction Module -- 2.5 Training and Inference -- 3 Experiments -- 3.1 Dataset and Preprocessing -- 3.2 Baseline and Evaluation Metrics -- 3.3 Results Analysis -- 3.4 Ablation Study -- 3.5 Case Study -- 4 Conclusion -- References -- A Graph Transformer-Based Method for Predicting LncRNA-Disease Associations Using Matrix Factorization and Automatic Meta-Path Generation -- 1 Introduction -- 2 Materials and Methods -- 2.1 Baseline Datasets -- 2.2 Similarity Networks -- 2.3 Fusion of Similarity Feature Matrices -- 2.4 LncRNA-Disease Heterogeneous Network -- 2.5 Generate Non-linear Features -- 2.6 Generate Linear Features -- 2.7 Generating Topological Features -- 2.8 Predicting Potential LDAs -- 3 Experiments and Results -- 3.1 Evaluation Metrics -- 3.2 Parameter Selection -- 3.3 Ablation Experiments -- 3.4 Comparison with Other Methods -- 3.5 Case Studies -- 4 Conclusion -- References -- The Dynamic Spatiotemporal Features Based on Rich Club Organization in Autism Spectrum Disorder -- 1 Introduction -- 2 Materials and Preprocessing -- 2.1 Participants -- 2.2 Diagnostic -- 2.3 fMRI Acquisition -- 2.4 Data Preprocessing -- 2.5 Construction of Dynamic Functional Connectivity Brain Network -- 3 Methods -- 3.1 Rich-Club Organization. 3.2 Dynamic Brain Networks Rich-Club Spatio-Temporal Similarity Metrics -- 3.3 Dynamic Rich-Club Brain Region Importance Assessment Indicator -- 3.4 Network Topology Characteristics -- 3.5 Construction and Classification of Dynamic Feature Sets Based on Rich-Club -- 4 Result -- 4.1 Temporal-Spatial Similarity Measurement of Dynamic Brain Networks -- 4.2 SVM Classification Results -- 4.3 Discussion -- 5 Conclusion -- References -- Integrated Analysis of Autophagy-Related Genes Identifies Diagnostic Biomarkers and Immune Correlates in Preeclampsia -- 1 Introduction -- 2 Materials and Methods -- 3 Results -- 4 Discussion -- References -- Multi-grained Cross-Modal Feature Fusion Network for Diagnosis Prediction -- 1 Introduction -- 2 Method -- 2.1 Fine-Grained Representation Learning Module -- 2.2 Fine-Grained Feature Fusion Module -- 2.3 Coarse-Grained Representation Learning Module -- 2.4 Coarse-Grained Feature Fusion Module -- 2.5 Prediction Module -- 3 Experiments and Results -- 3.1 Data Description -- 3.2 Implementation Details and Evaluation Metric -- 3.3 Baselines -- 3.4 Main Results -- 3.5 Ablation Study -- 3.6 Case Study -- 4 Conclusion -- References -- MOL-MOE: Learning Drug Molecular Characterization Based on Mixture of Expert Mechanism -- 1 Introduction -- 2 Method -- 2.1 Atomic and Functional Group Feature Fusion Based on Cross Attention -- 2.2 MOE for Drug Molecule Modeling -- 3 Results -- 3.1 Dataset -- 3.2 Experimental Setup -- 3.3 Experimental Results -- 3.4 Ablation Experiment -- 3.5 Comparative Experiments -- 3.6 Case Study -- 4 Conclusion -- References -- A Multimodal Federated Learning Framework for Modality Incomplete Scenarios in Healthcare -- 1 Introduction -- 2 Methodology -- 2.1 Problem Definition -- 2.2 Overview -- 2.3 Cluster Stepwise Aggregation -- 2.4 Prototype Contrastive Integration -- 3 Experiments. 3.1 Datasets and Data Preprocessing. |
| Record Nr. | UNINA-9910874669103321 |
|
Peng Wei
|
||
| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bioinformatics Research and Applications : 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024, Proceedings, Part III
| Bioinformatics Research and Applications : 20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024, Proceedings, Part III |
| Autore | Peng Wei |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (159 pages) |
| Altri autori (Persone) |
CaiZhipeng
SkumsPavel |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 981-9750-87-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part III -- Feddaw: Dual Adaptive Weighted Federated Learning for Non-IID Medical Data -- 1 Introduction -- 2 Method -- 2.1 Client-Side Classification Layer Probability Weighting Factor Adjustment Module -- 2.2 Server-Side Accuracy-Based Adaptive Weight Aggregation Module -- 3 Experiments and Results -- 3.1 Data Description -- 3.2 Non-IID Dataset Segmentation -- 3.3 Baselines and Implementation Details -- 3.4 Performance Comparison with Baseline Methods -- 4 Conclusion -- References -- LoopNetica: Predicting Chromatin Loops Using Convolutional Neural Networks and Attention Mechanisms -- 1 Introduction -- 2 Results -- 2.1 LoopNetica: Effectively Combines Convolutional Neural Networks and Attention Mechanisms -- 2.2 LoopNetica Can Accurately Predict Chromatin Loops -- 2.3 The LoopNetica Model Performs Exceptionally Well in Scenarios with Extremely Imbalanced Positive and Negative Samples -- 2.4 LoopNetica Successfully Captures Sequence Features and Discovers Type-Specific Motifs -- 3 Methods -- 3.1 Data Preparation -- 3.2 LoopNetica Model -- 3.3 Training Strategy -- 4 Discussion -- 5 Conclusion -- References -- Probabilistic and Machine Learning Models for the Protein Scaffold Gap Filling Problem -- 1 Introduction -- 2 Methodology -- 2.1 Data Collection -- 2.2 Data Preprocessing -- 2.3 The Proposed Models for the PSGF Problem with Known Gap Size -- 2.4 A Probabilistic Algorithm for the PSGF Problem with Known Gap Mass -- 3 Experimental Results -- 3.1 Results for PSGF with Known Gap Size Using ML Models -- 3.2 Results for PSGF with Known Gap Masses Using the Probabilistic Algorithm -- 4 Conclusions -- References -- Patient Anticancer Drug Response Prediction Based on Single-Cell Deconvolution -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Gene Expression Data Deconvolution.
3.2 Domain Invariant Feature Extraction -- 3.3 Training Classifier -- 4 Experiment -- 4.1 Drug Response Prediction for Clinical TCGA Dataset -- 4.2 Results of Ablation Experiments -- 5 Conclusion -- References -- A Data Set of Paired Structural Segments Between Protein Data Bank and AlphaFold DB for Medium-Resolution Cryo-EM Density Maps: A Gap in Overall Structural Quality -- 1 Introduction -- 2 Methods -- 3 Results and Discussion -- 3.1 The Dataset of Matched Structural Segments in the PDB/AlphaFold DB -- 3.2 Evaluation of Matched Structural Segments Using MolProbity -- 3.3 Differences in Local Quality Between the Four Models Derived from Medium-Resolution Cryo-EM Maps and Those Predicted with AlphaFold -- 4 Conclusion -- References -- PmmNDD: Predicting the Pathogenicity of Missense Mutations in Neurodegenerative Diseases via Ensemble Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Overall Workflow -- 2.2 Dataset Construction -- 2.3 Feature Extraction -- 2.4 PmmNDD Model Training -- 2.5 Evaluation Metrics -- 3 Results and Discussion -- 3.1 Performance of Different Ensemble Models of PmmNDD -- 3.2 Analysis of Feature Importance -- 3.3 Comparison with Existing Prediction Methods -- 3.4 Predictions for 3 Million Missense Mutations in NDDs -- 4 Conclusions -- References -- Improved Inapproximability Gap and Approximation Algorithm for Scaffold Filling to Maximize Increased Duo-Preservations -- 1 Introduction -- 2 Preliminaries -- 3 An Improved Inapproximability Gap for SF-MIDP -- 4 Approximation Algorithms for SF-MIDP -- 4.1 An Approximation Algorithm for the SF-MIDP -- 4.2 Proof of the Approximation Ratio -- 5 Experimental Results -- 6 Conclusion -- References -- Residual Spatio-Temporal Attention Based Prototypical Network for Rare Arrhythmia Classification -- 1 Introduction -- 2 Methods. 2.1 Residual Spatio-Temporal Attention Feature Extractor -- 2.2 Meta Training Based on Prototype Network -- 2.3 Meta Test -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Experiment Settings -- 3.3 Performance Comparison with Other ECG Few-Shot Methods -- 3.4 Ablation Experiments -- 3.5 Feature Extractor Performance on Common Classes Comparison with Baselines -- 3.6 Meta Test with Different Classifier -- 3.7 Visualization Analysis -- 4 Conclusion -- References -- SEMQuant: Extending Sipros-Ensemble with Match-Between-Runs for Comprehensive Quantitative Metaproteomics -- 1 Introduction -- 2 Methods -- 2.1 Overview of SEMQuant -- 2.2 Implementation and Software Test -- 3 Experiments and Results -- 3.1 Evaluation Measures -- 3.2 Benchmark Datasets and Experiment Design -- 3.3 Parameters for Benchmarking Software -- 3.4 Assessment of the False Positives of Transferred Peptides Using the Two-Organism Dataset -- 3.5 Assessment of the Identification and Quantification Results Using the Yeast-UPS1 Datasets -- 3.6 Assessment of the Identification and Quantification Results Using the In-House Dataset of a Four-Bacteria Mixed Culture -- 3.7 Assessment of the Identification and Quantification Results Using Two Mock Community Datasets -- 4 Conclusion -- 5 Data Availability -- References -- PrSMBooster: Improving the Accuracy of Top-Down Proteoform Characterization Using Deep Learning Rescoring Models -- 1 Introduction -- 2 Method -- 2.1 Basic Feature Extraction -- 2.2 Rescoring Model -- 3 Result and Discussion -- 3.1 Dataset and Preprocessing -- 3.2 Evaluation Criteria -- 3.3 Comparison of PrSM Results Before and After Rescoring -- 4 Conclusion -- References -- FCMEDriver: Identifying Cancer Driver Gene by Combining Mutual Exclusivity of Embedded Features and Optimized Mutation Frequency Score -- 1 Introduction -- 2 Materials and Methods. 2.1 Datasets and Resources -- 2.2 Networks Construction and Network Embedding -- 2.3 Gene Clustering to Detect the Modules of Highly Correlated Genes -- 2.4 Module Importance Score with Mutual Exclusivity -- 2.5 Comprehensive Scoring to Prioritize Driver Genes -- 3 Results and Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910874672303321 |
|
Peng Wei
|
||
| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||