دانلود کتاب شبکه‌های عصبی گراف برای اختلالات عصبی – اصول، کاربردها و مزایا در تحقیق و تشخیص

Chapter 1 explores the fundamentals of GNNs, a powerful machine learning technique for modeling complex relationships in graph-structured data. We cover core architectures such as GCNs, GATs, and GraphSAGE, along with advanced models like GINs and R-GCNs. Additionally, we discuss key optimization techniques for large-scale graphs and address challenges like scalability and interpretability. The chapter also highlights real-world applications in social networks, bioinformatics, and recommendation systems, emphasizing the transformative potential of GNNs in various domains. Chapter 2 explores the transformative potential of machine learning in revolutionizing neurology care, offering enhanced diagnosis, disease progression prediction, and personalized treatments. Successful integration of ML into clinical practice requires navigating a complex landscape of ethical, technological, and collaborative challenges as algorithms analyze increasingly sophisticated neurological data—ranging from neuroimaging to genetic markers and behavioral features. This chapter advocates for an interdisciplinary approach to address persistent issues such as data bias, patient privacy, the opacity of black-box systems, and the need for updated regulatory frameworks while emphasizing transparency and human oversight. The aim is to advance data quality, develop interpretable designs, and embed ethical considerations to fully realize ML’s potential in neurology. Chapter 3 examines the interplay between brain connectomics and graph theory in the mapping and analysis of neural networks. It emphasizes their significance in comprehending cognitive functioning and neurological illnesses, providing insights for clinical applications and individualized therapies. Chapter 4 examines the role of GNNs in advancing the field of brain connectomics, focusing on how these networks can improve our understanding of brain connectivity and its disruptions in neurological disorders. Brain connectomics consists of structural, functional, and effective connectivity, which can be analyzed using GNNs. These networks provide a sophisticated framework to analyze complex, non-Euclidean data, enabling exploration of relationships between brain regions and their functions. The research demonstrates that GNNs offer superior performance in disease diagnosis and cognitive performance prediction. For instance, GNNs predicted Alzheimer’s disease with 87% accuracy versus 79% for SVM. Similar improvements were seen for Parkinson’s and schizophrenia. Challenges such as data quality, model interpretability, and computational scalability remain. Chapter 5 explores the application of GNNs in classifying and diagnosing neurological diseases by leveraging their ability to model complex brain region relationships in neuroimaging data. Unlike traditional methods, GNNs capture non-Euclidean patterns, enhancing early disease detection, progression prediction, and personalized treatment. The chapter also discusses challenges such as data scarcity, computational demands, and model interpretability while highlighting future research directions. Chapter 6 investigates how multimodal data GNNs could combine several kinds of data to address practical challenges. Many problems in healthcare, autonomous systems, and e-commerce require merging multiple data sources—text, images, and sensor measurements. Using their representation as a graph, GNNs provide a means of data processing. Key ideas of GNNs and several multimodal integration methods—including early, late, hybrid, attention, and tensor fusion—are introduced. Applications in social networking, self-driving cars, e-commerce, and healthcare are covered. The chapter emphasizes challenges such as data homogeneity, scalability, missing data, and interpretability, and suggests future research paths, including scalability improvements, interpretability advancements, and real-time applications. In Chap. 7, GNNs have made strides in learning complex relationships between biological entities like genes, proteins, and metabolites for accurate biomarker discovery. GNNs are applied to gene expression and protein interaction data to identify key biomarkers of cancer and other diseases. Findings show that GNNs outperform traditional models: accuracy 92% vs. 75% (SVM) and 78% (Random Forest), with higher precision and recall. This suggests GNNs can enhance biomarker discovery and precision medicine. Chapter 8 explores applications in cognitive neuroscience, focusing on computational and neuroimaging approaches to memory and learning. The chapter defines cognitive processes and examines how neuroscience techniques help unravel human cognition. Cutting-edge computational models and neuroimaging methods for studying memory and learning are discussed, highlighting applications that enhance understanding of brain function and bridge theoretical neuroscience with practical applications. Chapter 9 explores how GNNs integrate with several modalities of multimodal neuroimaging data, including structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), genetic biomarkers, and clinical assessments. The framework improves disease classification accuracy and discovers individualized biomarkers through architectures like GCNs and GATs. It delivers robustness and flexibility across neurological conditions (Alzheimer’s, Parkinson’s, and epilepsy) and outperforms previous models (CNNs and RNNs) with an accuracy of 89%, precision of 87%, recall of 90%, and F1-score of 88%. GNN demonstrates the highest precision (90%) and recall (88%) for AD. This chapter contributes to computational neuroscience and AI-driven neurological diagnostics, benefiting researchers, clinicians, and data scientists. Chapter 10 explores the use of GNNs in identifying potential calpain-10 inhibitors for neurological disorder therapy. Computational methods such as AlphaFold, DeepBindPoc, and DeepBindGCN provide insights into protein structure and ligand interactions, and AI is transforming drug development. The study aims to discover active binding sites, predict receptor structure, and screen therapeutic options using molecular dynamics and AI modeling, improving virtual screening accuracy. This chapter advances AI-assisted medication development and opens the door to novel treatments. Chapter 11 delves into the foundational concepts of GNNs, highlighting their superiority over traditional non-graph-based approaches in handling multimodal and longitudinal clinical data. We explore potential applications in structuring patient networks, modeling disease progression, and uncovering biochemical interactions. Real-world applications illustrate how GNNs contribute to disease classification, patient subgroup identification, and drug discovery—accelerating drug repurposing and improving treatment strategies. Despite challenges in clinical adoption, the chapter provides insights to overcome them, ensuring GNNs can be effectively integrated into medical practice and contribute to precision medicine.