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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 I / / edited by De-Shuang Huang, Qinhu Zhang, Jiayang Guo"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"981-9756-89-8"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (490 pages)"]
[null,null,"Collana",null,"Lecture Notes in Bioinformatics, , 2366-6331 ; ; 14881"]
[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"]
[{"$nulls":4},"Computational and Systems Biology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Biomedical Data Modeling and Mining -- Alzheimer's Disease Diagnosis via Specific-Shared Representation Learning in Multimodal Neuroimaging -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Shallow Feature Learning -- 2.3 Modality Specific Representation Learning -- 2.4 Modality Shared Representation Learning -- 2.5 Modality Specific-Shared Representation Learning -- 3 Experiments -- 3.1 Materials and Image Pre-processing -- 3.2 Comparison Methods -- 3.3 Experimental Setup -- 3.4 Evaluation of Automated Diseases Diagnosis -- 3.5 Ablation Study -- 4 Conclusion -- References -- An Activity Graph-Based Deep Convolutional Neural Network Framework in Symptom Severity Diagnosis Towards Parkinson's Disease Using Inertial Sensors -- 1 Introduction -- 2 Subjects and Data Collection -- 2.1 Participants -- 2.2 Data Collection -- 3 Methodology -- 3.1 Activity Graph Generation -- 3.2 Data Augmentation -- 3.3 Convolutional Neural Network -- 4 Results -- 5 Discussion and Conclusion -- References -- An Optimization Method for Drug Design Based on Molecular Features -- 1 Introduction -- 2 Methods -- 2.1 Pocket of Targeted Protein -- 2.2 Feature Extraction of Targeted Protein -- 2.3 Feature Representation of Drug Molecule -- 2.4 Model "]
[{"$nulls":4},"-- 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."]
[{"$nulls":4},"3.4 Gut Microbiota Data Analysis with Chat2GM Module -- 4 Conclusions -- References -- CVAE-Based Hybrid Sampling Data Augmentation Method and Interpretation for Imbalanced Classification of Gout Disease -- 1 Introduction -- 2 Materials and Methods -- 2.1 CVAE-Based Hybrid Sampling -- 2.2 Detection Model -- 2.3 Interpretation -- 3 Experiment and Result -- 3.1 Datasets -- 3.2 Classification Results -- 3.3 Comparison of Balancing Strategies -- 3.4 Model Interpretation -- 4 Conclusion -- References -- DepthParkNet: A 3D Convolutional Neural Network with Depth-Aware Coordinate Attention for PET-Based Parkinson's Disease Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Depth-Aware Coordinate Attention -- 2.2 PDaug -- 2.3 Class-Balanced Loss -- 3 Experiments -- 3.1 Datasets and Preprocessing -- 3.2 Implementation Details -- 3.3 Comparison -- 3.4 Ablation Study -- 4 Conclusion -- References -- Gene Selection and Classification Method Based on SNR and Multi-loops BPSO -- 1 Introduction -- 2 Method -- 2.1 The Multi-loops BPSO -- 3 Experiments and Results -- 3.1 Experiment Preparation -- 3.2 Experimental Design Principles -- 3.3 Preprocessing by SNR -- 3.4 The Comparison of One-Loop and Multi-loops on BPSO -- 3.5 Comparative Experiment and Analysis -- 4 Conclusion -- References -- Graph Convolutional Networks Based Multi-modal Data Integration for Breast Cancer Survival Prediction -- 1 Introduction -- 2 Method -- 2.1 Feature Selection and Fusion -- 2.2 Patient-Patient Graph Construction -- 2.3 Multi-modal Graph Convolutional Networks Module -- 2.4 Training Details -- 3 Experiments -- 3.1 Datasets and Evaluation Metrics -- 3.2 Comparisons with State-of-The-Art -- 3.3 Ablation Studies -- 3.4 Validation -- 4 Conclusion and Future Work -- References -- IDHPre: Intradialytic Hypotension Prediction Model Based on Fully Observed Features -- 1 Introduction."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Graph Convolutional Autoencoder for Multi-omics Data Integration and Classification -- 1 Introduction -- 2 Methods -- 2.1 Overview of SGEGCAE -- 2.2 AE for Attribute Information Representation -- 2.3 EGCAE for Feature Representations -- 2.4 Sparse Gating Strategy for Enhanced Feature Representations -- 2.5 TCP for Omics Informativeness Estimation -- 2.6 TFN for Multi-omics Integration -- 3 Experiments and Results -- 3.1 Datasets and Evaluation Metrics -- 3.2 Analysis of Classification Results -- 3.3 Ablation Studies -- 3.4 Analysis of Hyper-parameter."]
[{"$nulls":4},"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."]
[{"$nulls":4},"2.4 Pairwise Judgment Module -- 3 Experiments -- 3.1 Datasets -- 3.2 Experimental Settings -- 3.3 Experimental Results. -- 3.4 Parameter Sensitivity Analysis. -- 4 Conclusion -- References -- A Retrieval-Based Molecular Style Transformation Optimization Model -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Molecular Retriever -- 2.3 Information Fusion Module and Decoder -- 2.4 Retrieval-Based Molecular Style Transformation Generative Network -- 3 Results -- 3.1 Datasets and Performance Metrics -- 3.2 Results on the QED and PlogP Tasks -- 3.3 Ablation Experiments -- 3.4 Visualized Optimization Results -- 3.5 Parameter Analysis -- 4 Conclusion -- References -- Aggregation Strategy with Gradient Projection for Federated Learning in Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Problem Definition -- 2.2 Federal Projection Matrix -- 2.3 Local Training with GPM -- 3 Experiment -- 3.1 Datasets and Experiment Settings -- 3.2 Implementation Details -- 3.3 Evaluation and Discussion -- 3.4 Ablation Studies -- 4 Conclusion -- References -- Coronary Artery 3D/2D Registration Based on Particle Swarm Optimization of Contextual Morphological Features -- 1 Introduction -- 2 Proposed Method -- 2.1 DSA Vessel Intersection Extraction -- 2.2 CTA Vessel Intersection Extraction -- 2.3 3D-2D Vessel Matching Based on PSO -- 3 Experiments and Results -- 3.1 DSA Vessel Intersection Extraction "]
[{"$nulls":4},"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."]
[{"$nulls":4},"3.2 Möbius Representation Transformation."]
[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. ."]