05415nam 22005655 450 991103932120332120251101120416.09783032043153(electronic bk.)978303204314610.1007/978-3-032-04315-3(MiAaPQ)EBC32384782(Au-PeEL)EBL32384782(CKB)41996879700041(DE-He213)978-3-032-04315-3(EXLCZ)994199687970004120251101d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierGraph Neural Networks for Neurological Disorders Fundamentals, Applications and Benefits in Research and Diagnostics /edited by Md. Mehedi Hassan, Anindya Nag, Shariful Islam, Herat Joshi1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (0 pages)Medicine SeriesPrint version: Hassan, Mehedi Graph Neural Networks for Neurological Disorders Cham : Springer,c2025 9783032043146 Understanding Graph Neural Networks: Foundations and Applications -- Neurological Disorders: An Overview of Classification and Diagnosis -- Graph Theory Fundamentals for Brain Network Modeling -- Graph Neural Network Architectures: A Comprehensive Review -- Genetic Influences on Brain Connectivity and Neurological Disorders -- Multi-modal Neuroimaging Data Fusion for GNNs -- Predictive Modeling of Neurological Disease Progression -- Diagnostic Applications of Graph Neural Networks -- Personalized Medicine Approaches in Neurology -- Ethical Considerations in GNN Research for Neurological Disorders -- Network Neuroscience: Bridging Gaps in Understanding Brain Connectivity -- GNNs for Studying Cognitive Disorders: Alzheimer's Disease and Dementia -- Parkinson's Disease: Insights from Graph Neural Network Analysis -- GNNs in Epilepsy Research: Seizure Prediction and Classification -- Neurodevelopmental Disorders and GNN Applications -- Brain Tumor Analysis using Graph Neural Networks -- Stroke and GNN-based Rehabilitation Strategies -- GNNs for Understanding Neurodegenerative Disorders -- Neuropsychiatric Disorders: Insights from Graph Neural Network Analysis -- Future Directions and Challenges in GNN Research for Neurology.This book represents a unique and comprehensive resource for understanding the intersection of advanced artificial intelligence (AI) and neurology. By focusing on graph neural networks (GNNs), the book addresses a crucial gap in the current literature, providing valuable insights into the analysis and interpretation of complex brain networks and neurological data. Intended for a diverse audience, including clinicians, scientists, researchers, and students, it demystifies the complexities of GNNs and their applications in neurology. For clinicians and healthcare practitioners, the book illustrates how GNNs can enhance diagnostic accuracy, inform personalized treatment plans and predict disease progression. This leads to improved patient outcomes and a deeper understanding of neurological conditions such as Alzheimer's, Parkinson's, multiple sclerosis and epilepsy. Researchers will find the book particularly valuable as it delves into the methodologies and technical aspects of GNNs, showcasing their ability to handle diverse data sources including genetic, imaging and clinical information. By integrating these datasets, GNNs reveal hidden patterns and biomarkers, offering new avenues for research and potential therapeutic targets. A Guide to Graph Neural Networks for Neurological Disorders addresses the challenge of missing data, a common issue in neurological research, and demonstrates how GNNs can manage and mitigate these gaps. For students, both undergraduate and postgraduate, the book serves as an educational tool, providing clear explanations and practical examples that make complex concepts accessible. It equips the next generation of neuroscientists and data scientists with the knowledge and skills needed to contribute to this rapidly evolving field. The book aims to provide a foundational understanding of GNNs, demonstrate their practical applications in neurology, and inspire further research and innovation. By bridging the gap between AI and medical practice, the book empowers readers to leverage cutting-edge technology in the quest to understand and treat neurological illnesses, ultimately enhancing the quality of care and advancing the field of neuroscience.Medicine SeriesMedical informaticsNeurosciencesNeural networks (Computer science)Health InformaticsNeuroscienceMathematical Models of Cognitive Processes and Neural NetworksMedical informatics.Neurosciences.Neural networks (Computer science).Health Informatics.Neuroscience.Mathematical Models of Cognitive Processes and Neural Networks.610.285Hassan Mehedi1833381MiAaPQMiAaPQMiAaPQ9911039321203321Graph Neural Networks for Neurological Disorders4454531UNINA05769nam 22006375 450 99669166140331620251116120403.0978303208452110.1007/978-3-032-08452-1(CKB)43368587800041(MiAaPQ)EBC32419982(Au-PeEL)EBL32419982(DE-He213)978-3-032-08452-1(EXLCZ)994336858780004120251116d2026 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierBioinformatics and Biomedical Engineering 12th International Conference, IWBBIO 2025, Gran Canaria, Spain, July 16–18, 2025, Proceedings, Part II /edited by Ignacio Rojas, Francisco Ortuño, Fernando Rojas Ruiz, Luis Javier Herrera, Olga Valenzuela, Juan José Escobar1st ed. 2026.Cham :Springer Nature Switzerland :Imprint: Springer,2026.1 online resource (458 pages)Lecture Notes in Bioinformatics,2366-6331 ;160519783032084514 -- Biosensors and Data Acquisition. -- Methods for Wearable Electrocardiogram and Photoplethysmogram Synchronization. -- Optimization of the Tau Parameter in Phase Space Plots for ECG Signal Quality Assessment. -- Use of the graphic tablet for monitoring and analysis of the spatial and temporal characteristics of a precise manual task. -- Emerging Trends and Innovations in E-Health. -- Vision Language Models for Dynamic Human Activity Recognition in Healthcare Settings. -- Method for detection and analysis of the sit-to-walk transition in older adults. Threshold-based transition detection application: a case study. -- Agent-Based Modeling of Retinal Development. -- High Performance in Bioinformatics. -- Benchmarking variant calling algorithms for the analysis of genomic data in panel sequencing. -- VCFX: A Minimalist, Modular Toolkit for Streamlined Variant Analysis. -- Machine Learning-Based Screening Tool for Lung Adenocarcinoma Via Gut Microbiome. -- Cost-Effective Microbiome Profiling: Abridged Shotgun Sequencing. -- Correlation between CYP1A2 Genetic Polymorphism and Drug ResponseI. -- A Systematic Comparison of Phylogenetic Inference Methods Using an Inverse Problem Approach. -- Innovations in Cancer Research: The Role of Bioinformatics and Biomedical Engineering. -- Differential Flux-balance Analysis infers metabolic mutations associated with cancer. -- Explainable AI for Clinical Decision-Making: Unlocking the Potential of MSI Thresholds in Bladder Cancer. -- IGHV Mutational Status and DNA Entropy: Refining Prognostic Tools in Chronic Lymphocytic Leukemia. -- Machine learning in Bioinformatics and Biomedicine. -- Machine Learning Models for Assessing Depression in Syrian Adolescent Refugees in Jordan. -- Predicting T cell receptor specificity with graph attention networks. -- CyberKnife and Data Mining: Exploring opportunities for clinical advancements. -- Sickle cell disease patient care system using artificial intelligence. -- Automated Annotation of Electronic Health Records Using Large Language Models. -- A supervised learning strategy to investigate age effect on brain activity and support biomarkers detection for neurological disorders. -- Recent Advances in Bioinformatics. -- NucleoConvert Analytics: An Integrated Platform for DNA-to-RNA Conversion and Sequence Analysis. -- Computational Analysis and Prediction of CYP1A2-Related Toxicants for Safer Drug Discovery. -- Gene Co-Expression Network analysis based on GPUs for biomarkers discovery in sarcomas. -- An Algorithm For Local Pairwise Alignment Of DNA Sequences. -- Metabolomic Predictions via SOM: A Cold-Stress Case Study in Arabidopsis thaliana.This two-volume set LNBI 16050-16051 constitutes the proceedings of the 12th International Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2025, held in Canaria, Spain, during July 16–18, 2025. The 57 full papers presented in these volumes were carefully reviewed and selected from 98 submissions. They were organized into the following topical sections: Part I: Advances in Deep Learning in Bioinformatics and Bioengineering; Bioinformatics and Biomedical Applications; Biomarker Identification; Biomedical Computing; and Biomedical Engineering. Part II: Biosensors and Data Acquisition; Emerging Trends and Innovations in E-Health; High Performance in Bioinformatics; Innovations in Cancer Research: The Role of Bioinformatics and Biomedical Engineering; Machine Learning in Bioinformatics and Biomedicine; and Recent Advances in Bioinformatics.Lecture Notes in Bioinformatics,2366-6331 ;16051BioinformaticsComputer networksEngineeringData processingBiomedical engineeringBioinformaticsComputational and Systems BiologyComputer Communication NetworksData EngineeringBiomedical Engineering and BioengineeringBioinformatics.Computer networks.EngineeringData processing.Biomedical engineering.Bioinformatics.Computational and Systems Biology.Computer Communication Networks.Data Engineering.Biomedical Engineering and Bioengineering.570.285Rojas Ignacio1299449MiAaPQMiAaPQMiAaPQBOOK996691661403316Bioinformatics and Biomedical Engineering4466466UNISA