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'Essentials of Cancer Genomic, Computational Approaches and Precision Medicine / / edited by Nosheen Masood, Saima Shakil Malik
| 'Essentials of Cancer Genomic, Computational Approaches and Precision Medicine / / edited by Nosheen Masood, Saima Shakil Malik |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XVIII, 499 p. 59 illus., 50 illus. in color.) |
| Disciplina | 616.994042 |
| Soggetto topico |
Cancer - Research
Oncology Human genetics Bioinformatics Nanotechnology Genètica humana Càncer Oncologia Bioinformàtica Nanotecnologia Cancer Research Human Genetics Nanotechnology and Microengineering |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-15-1067-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Module 1_ Introduction -- Module 2_ Cancer Metastasis -- Module 3_ Role of Transcriptomics in cancer treatment strategies -- Module 4_Computational approaches -- Module 5_Biostatistics and clinical oncology -- Module 6_Pharmacogenomics in cancer -- Chapter 7_Case studies. |
| Record Nr. | UNINA-9910409704903321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced Intelligent Computing in Bioinformatics : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part I / / edited by De-Shuang Huang, Qinhu Zhang, Jiayang Guo
| Advanced Intelligent Computing in Bioinformatics : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part I / / edited by De-Shuang Huang, Qinhu Zhang, Jiayang Guo |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (490 pages) |
| Disciplina | 572.80285 |
| Collana | Lecture Notes in Bioinformatics |
| Soggetto topico |
Computational intelligence
Artificial intelligence Bioinformatics Computational Intelligence Artificial Intelligence Computational and Systems Biology Bioinformàtica Intel·ligència computacional Intel·ligència artificial |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 981-9756-89-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Biomedical Data Modeling and Mining -- Alzheimer's Disease Diagnosis via Specific-Shared Representation Learning in Multimodal Neuroimaging -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Shallow Feature Learning -- 2.3 Modality Specific Representation Learning -- 2.4 Modality Shared Representation Learning -- 2.5 Modality Specific-Shared Representation Learning -- 3 Experiments -- 3.1 Materials and Image Pre-processing -- 3.2 Comparison Methods -- 3.3 Experimental Setup -- 3.4 Evaluation of Automated Diseases Diagnosis -- 3.5 Ablation Study -- 4 Conclusion -- References -- An Activity Graph-Based Deep Convolutional Neural Network Framework in Symptom Severity Diagnosis Towards Parkinson's Disease Using Inertial Sensors -- 1 Introduction -- 2 Subjects and Data Collection -- 2.1 Participants -- 2.2 Data Collection -- 3 Methodology -- 3.1 Activity Graph Generation -- 3.2 Data Augmentation -- 3.3 Convolutional Neural Network -- 4 Results -- 5 Discussion and Conclusion -- References -- An Optimization Method for Drug Design Based on Molecular Features -- 1 Introduction -- 2 Methods -- 2.1 Pocket of Targeted Protein -- 2.2 Feature Extraction of Targeted Protein -- 2.3 Feature Representation of Drug Molecule -- 2.4 Model -- 3 Experimental Results -- 3.1 Datasets -- 3.2 Comparison of Experiments -- 4 Conclusion -- References -- Application of Machine Learning and Large Language Model Module for Analyzing Gut Microbiota Data -- 1 Introduction -- 2 Methodology -- 2.1 Overview -- 2.2 Machine Learning Algorithms -- 2.3 Chat2GM - a LLM Module Based on Langchain Framework -- 3 Applications and Analysis -- 3.1 Data -- 3.2 Species Diversity Analysis with Statistical Methods -- 3.3 Identification of Obesity-Related Biomarkers via Machine Learning.
3.4 Gut Microbiota Data Analysis with Chat2GM Module -- 4 Conclusions -- References -- CVAE-Based Hybrid Sampling Data Augmentation Method and Interpretation for Imbalanced Classification of Gout Disease -- 1 Introduction -- 2 Materials and Methods -- 2.1 CVAE-Based Hybrid Sampling -- 2.2 Detection Model -- 2.3 Interpretation -- 3 Experiment and Result -- 3.1 Datasets -- 3.2 Classification Results -- 3.3 Comparison of Balancing Strategies -- 3.4 Model Interpretation -- 4 Conclusion -- References -- DepthParkNet: A 3D Convolutional Neural Network with Depth-Aware Coordinate Attention for PET-Based Parkinson's Disease Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Depth-Aware Coordinate Attention -- 2.2 PDaug -- 2.3 Class-Balanced Loss -- 3 Experiments -- 3.1 Datasets and Preprocessing -- 3.2 Implementation Details -- 3.3 Comparison -- 3.4 Ablation Study -- 4 Conclusion -- References -- Gene Selection and Classification Method Based on SNR and Multi-loops BPSO -- 1 Introduction -- 2 Method -- 2.1 The Multi-loops BPSO -- 3 Experiments and Results -- 3.1 Experiment Preparation -- 3.2 Experimental Design Principles -- 3.3 Preprocessing by SNR -- 3.4 The Comparison of One-Loop and Multi-loops on BPSO -- 3.5 Comparative Experiment and Analysis -- 4 Conclusion -- References -- Graph Convolutional Networks Based Multi-modal Data Integration for Breast Cancer Survival Prediction -- 1 Introduction -- 2 Method -- 2.1 Feature Selection and Fusion -- 2.2 Patient-Patient Graph Construction -- 2.3 Multi-modal Graph Convolutional Networks Module -- 2.4 Training Details -- 3 Experiments -- 3.1 Datasets and Evaluation Metrics -- 3.2 Comparisons with State-of-The-Art -- 3.3 Ablation Studies -- 3.4 Validation -- 4 Conclusion and Future Work -- References -- IDHPre: Intradialytic Hypotension Prediction Model Based on Fully Observed Features -- 1 Introduction. 2 Related Work -- 2.1 Imputation of Missing Values -- 2.2 Feature Selection -- 3 IDHPre -- 3.1 Imputation of Missing Values -- 3.2 Feature Selection -- 4 Experiment and Evaluation -- 4.1 Implementation Details -- 4.2 Qualitative and Quantitative Comparison -- 4.3 Ablation Study -- 5 Conclusion -- References -- Machine Learning Models for Improved Cell Screening -- 1 Introduction -- 2 Related Work -- 2.1 Mainstream Cell Line Screening Methods -- 2.2 Model Stacking -- 3 Dataset -- 4 Proposed Methods -- 4.1 Stacked Machine Learning Method (SMLM) -- 4.2 Simple Linear Method (SLM) -- 4.3 Model Characteristics and Applicability Analysis -- 5 Experimental Results -- 5.1 Experimental Setup -- 5.2 Experimental Analysis -- 6 Conclusion and Pen Question -- References -- Prediction of Bladder Cancer Prognosis by Deep Cox Proportional Hazards Model Based on Adversarial Autoencoder -- 1 Introduction -- 2 Methods -- 2.1 The Framework of the Study -- 2.2 Adversarial Autoencoders -- 2.3 The Architecture of AAE-Cox -- 3 Results -- 3.1 Datasets -- 3.2 Experiments -- 3.3 Evaluations of Cancer Outcomes Prediction -- 3.4 Method Comparison -- 3.5 Independent Test -- 3.6 Identification of Cancer-Related Prognostic Markers and Pathways -- 4 Conclusion and Discussion -- References -- SGEGCAE: A Sparse Gating Enhanced Graph Convolutional Autoencoder for Multi-omics Data Integration and Classification -- 1 Introduction -- 2 Methods -- 2.1 Overview of SGEGCAE -- 2.2 AE for Attribute Information Representation -- 2.3 EGCAE for Feature Representations -- 2.4 Sparse Gating Strategy for Enhanced Feature Representations -- 2.5 TCP for Omics Informativeness Estimation -- 2.6 TFN for Multi-omics Integration -- 3 Experiments and Results -- 3.1 Datasets and Evaluation Metrics -- 3.2 Analysis of Classification Results -- 3.3 Ablation Studies -- 3.4 Analysis of Hyper-parameter. 3.5 Analysis of Different Omics Data Types -- 4 Conclusion -- References -- Short-Term Blood Glucose Prediction Method Based on Signal Decomposition and Bidirectional Networks -- 1 Introduction -- 2 Short-Term Blood Glucose Prediction Method Based on Signal Decomposition and Bidirectional Networks -- 2.1 Overall Approach -- 2.2 Variation Mode Decomposition Algorithm Based on Sparrow Search -- 2.3 Composite Network of Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) -- 3 Results and Analysis -- 3.1 Experimental Environment and Parameter Settings -- 3.2 Model Performance Evaluation Metrics -- 3.3 Model Performance Evaluation Metrics -- 4 Conclusion -- References -- SLGNNCT: Synthetic Lethality Prediction Based on Knowledge Graph for Different Cancers Types -- 1 Introduction -- 2 Dataset -- 3 Method -- 3.1 Knowledge Graph Level Gene Embedding Generation -- 3.2 Message Aggregation Based on Factors -- 3.3 Calculation of Synthetic Lethal Interaction Probabilities -- 4 Experiment -- 4.1 Baselines -- 4.2 Model Evaluation -- 4.3 Results and Analysis of Ablation Experiments -- 5 Conclusion -- References -- TransPBMIL: Transformer-Based Weakly Supervised Prognostic Prediction in Ovarian Cancer with Pseudo-Bag Strategy -- 1 Introduction -- 2 Materials and Methods -- 2.1 Participants and Dataset Generation -- 2.2 TransPBMIL Framework -- 3 Result -- 3.1 Comparison with Existing Weakly Supervised Works -- 3.2 The Performance Improvement Brought by the Pseudo-Bag Strategy. -- 3.3 Visualization of Detection Results -- 4 Conclusion -- References -- Biomedical Informatics Theory and Methods -- A Heterogeneous Cross Contrastive Learning Method for Drug-Target Interaction Prediction -- 1 Introduction -- 2 Method -- 2.1 Graph Embedding Module -- 2.2 Self-contrast Module -- 2.3 Cross-Contrast Module. 2.4 Pairwise Judgment Module -- 3 Experiments -- 3.1 Datasets -- 3.2 Experimental Settings -- 3.3 Experimental Results. -- 3.4 Parameter Sensitivity Analysis. -- 4 Conclusion -- References -- A Retrieval-Based Molecular Style Transformation Optimization Model -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Molecular Retriever -- 2.3 Information Fusion Module and Decoder -- 2.4 Retrieval-Based Molecular Style Transformation Generative Network -- 3 Results -- 3.1 Datasets and Performance Metrics -- 3.2 Results on the QED and PlogP Tasks -- 3.3 Ablation Experiments -- 3.4 Visualized Optimization Results -- 3.5 Parameter Analysis -- 4 Conclusion -- References -- Aggregation Strategy with Gradient Projection for Federated Learning in Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Problem Definition -- 2.2 Federal Projection Matrix -- 2.3 Local Training with GPM -- 3 Experiment -- 3.1 Datasets and Experiment Settings -- 3.2 Implementation Details -- 3.3 Evaluation and Discussion -- 3.4 Ablation Studies -- 4 Conclusion -- References -- Coronary Artery 3D/2D Registration Based on Particle Swarm Optimization of Contextual Morphological Features -- 1 Introduction -- 2 Proposed Method -- 2.1 DSA Vessel Intersection Extraction -- 2.2 CTA Vessel Intersection Extraction -- 2.3 3D-2D Vessel Matching Based on PSO -- 3 Experiments and Results -- 3.1 DSA Vessel Intersection Extraction Results -- 3.2 Results of CTA Vascular Center Line and Intersection -- 3.3 Results of Vascular Matching Between CTA and DSA -- 4 Conclusions -- References -- Enhancing Drug-Drug Interaction Predictions in Biomedical Knowledge Graphs Through Integration of Householder Projections and Capsule Network Techniques -- 1 Introduction -- 2 Preliminaries -- 2.1 Projective Space -- 2.2 Advanced Formulation of Householder Projections -- 3 Model -- 3.1 Relational Householder Projections. 3.2 Möbius Representation Transformation. |
| Record Nr. | UNINA-9910878049003321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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
| 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 |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (505 pages) |
| Disciplina | 572.80285 |
| Collana | Lecture Notes in Bioinformatics |
| Soggetto topico |
Computational intelligence
Artificial intelligence Bioinformatics Computational Intelligence Artificial Intelligence Computational and Systems Biology Bioinformàtica Intel·ligència artificial Intel·ligència computacional |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 981-9756-92-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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.
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. 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 (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. 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. 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 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. 3 Experiments and Analyses. |
| Record Nr. | UNINA-9910878052503321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Bioengineering / / edited by Renu Vyas
| Advances in Bioengineering / / edited by Renu Vyas |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (229 p.) |
| Disciplina | 660.6 |
| Soggetto topico |
Biomedical engineering
Molecular biology Proteins Bioinformatics Human physiology Biomedical Engineering/Biotechnology Molecular Medicine Protein Structure Human Physiology Bioenginyeria Bioinformàtica Materials nanoestructurats Nanopartícules |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-15-2063-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Modeling of protein complexes involved in signaling pathway for Non-Small Cell Lung Cancer -- Chapter 2. Role of BioJava in the Department of Bioinformatics Tools -- Chapter 3. Overview of machine learning methods in ADHD prediction -- Chapter 4. Simplified Protein Structure Prediction Using Parallel Genetic Algorithms -- Chapter 5. Applications of deep learning in drug discovery -- Chapter 6. Big Data Analytics for Handling NGS Data & its Applications in Identifying Cancer Mutations -- Chapter 7. Medicinal Properties of Fruit waste -- Chapter 8. Epigenetic toxicity of nanoparticles -- Chapter 9. Protein Misfolding and Aggregation in Neurodegenerative diseases -- Chapter 10. Enzyme technology prospectus & their Biomedical Applications -- Chapter 11. Polyunsaturated fatty acids enhance the recovery of bone marrow impairment caused after radiation -- Chapter 12. Nanomaterial Enabled Rapid Electrochemical Biosensors For Bacterial Pathogens -- Chapter 13. Heart Rate Variability Analysis in lung cancer patients to study the effect of treatment -- Chapter 14. Co-Relation of Physiological Signals And Therapy for Diagnostics Purpose of Periodic Limb Movement Disorder (Plmd) -- Chapter 15. Analysis of Forward Head Posture -- Chapter 16. Biopolymeric Smart Nano-Carriers for Drug Delivery Applications. |
| Record Nr. | UNINA-9910409691903321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in intelligent information hiding and multimedia signal processing . Volume 1 : proceeding of the 16th international conference on IIHMSP in conjunction with the 13th international conference on FITAT, November 5-7, 2020, Ho Chi Minh City, Vietnam / / Jeng-Shyang Pan [and four others] editors
| Advances in intelligent information hiding and multimedia signal processing . Volume 1 : proceeding of the 16th international conference on IIHMSP in conjunction with the 13th international conference on FITAT, November 5-7, 2020, Ho Chi Minh City, Vietnam / / Jeng-Shyang Pan [and four others] editors |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (552 pages) |
| Disciplina | 006.3 |
| Collana | Smart Innovation, Systems and Technologies |
| Soggetto topico |
Computational intelligence
Tecnologia de la informació Intel·ligència computacional Intel·ligència artificial Mineria de dades Processament de senyals Bioinformàtica Internet de les coses |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 981-336-420-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484434303321 |
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in intelligent information hiding and multimedia signal processing Proceeding of the 16th International Conference on IIHMSP in conjunction with the 13th international conference on FITAT, November 5-7, 2020, Ho Chi Minh City, Vietnam . Volume 2 / / edited by Jeng-Shyang Pan [and three others]
| Advances in intelligent information hiding and multimedia signal processing Proceeding of the 16th International Conference on IIHMSP in conjunction with the 13th international conference on FITAT, November 5-7, 2020, Ho Chi Minh City, Vietnam . Volume 2 / / edited by Jeng-Shyang Pan [and three others] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (546 pages) |
| Disciplina | 006.3 |
| Collana | Smart Innovation, Systems and Technologies |
| Soggetto topico |
Computational intelligence
Artificial intelligence Signal processing Tecnologia de la informació Processament de senyals Mineria de dades Intel·ligència computacional Intel·ligència artificial Bioinformàtica Internet de les coses |
| Soggetto genere / forma |
Llibres electrònics
Congressos |
| ISBN | 981-336-757-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484358703321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applications of bioinformatics in rice research / / edited by Manoj Kumar Gupta, Lambodar Behera
| Applications of bioinformatics in rice research / / edited by Manoj Kumar Gupta, Lambodar Behera |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (363 pages) |
| Disciplina | 371 |
| Soggetto topico |
Life sciences
Arròs Bioinformàtica Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-3997-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910502642403321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applied Statistical Considerations for Clinical Researchers / / by David Culliford
| Applied Statistical Considerations for Clinical Researchers / / by David Culliford |
| Autore | Culliford David |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (249 pages) : illustrations |
| Disciplina | 610.727 |
| Soggetto topico |
Medical informatics
Biometry Bioinformatics Medicine - Research Biology - Research Health Informatics Biostatistics Biomedical Research Estadística mèdica Bioinformàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030874100
9783030874094 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Preliminaries -- Design -- Planning -- Data Acquisition -- Data Manipulation Analysis -- Inferencesty -- Dissemination -- A Case Study -- Conclusions. |
| Record Nr. | UNINA-9910523772003321 |
Culliford David
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Association Analysis Techniques and Applications in Bioinformatics / / by Qingfeng Chen
| Association Analysis Techniques and Applications in Bioinformatics / / by Qingfeng Chen |
| Autore | Chen Qingfeng |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (396 pages) |
| Disciplina | 570.285 |
| Soggetto topico |
Data mining
Expert systems (Computer science) Machine learning Bioinformatics Big data Medical informatics Data Mining and Knowledge Discovery Knowledge Based Systems Machine Learning Computational and Systems Biology Big Data Health Informatics Bioinformàtica Aprenentatge automàtic Sistemes experts (Informàtica) Informàtica mèdica Mineria de dades Dades massives |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 9789819982516 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter1:Computer science for Molecular biology -- Chapter2:Introduction to association analysis -- Chapter3:Introduction to computational linguistics and biology structure -- Chapter4:Matrix decomposition for dimensionality deduction -- Chapter5:Discovering conserved RNA secondary structures with structure similarity -- Chapter6:Gene ontology for non-coding RNAs classification -- Chapter7:Learning frequent sub-structure by graph mining -- Chapter8:Editing distance and its application to biology graph analytics -- Chapter9:Sequence assembly and applications -- Chapter10:Classifying protein structures by measuring structural similarity -- Chapter11:Identification of metabolic pathways with embedding network -- Chapter12:Emerging Knowledge integration-based approach with multi-sources data for bioinformatics -- Chapter13:Conclusion and Future Work. |
| Record Nr. | UNINA-9910855365003321 |
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Chen Qingfeng
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Bioinformatics and computational biology : a primer for biologists / / Basant K. Tiwary
| Bioinformatics and computational biology : a primer for biologists / / Basant K. Tiwary |
| Autore | Tiwary Basant K. |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (239 pages) |
| Disciplina | 570.285 |
| Soggetto topico |
Bioinformatics
Computational biology Bioinformàtica Biologia computacional |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
981-16-4240-0
981-16-4241-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743247203321 |
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Tiwary Basant K.
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| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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