Machine Learning Methods For Multi Omics Data Integration
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Machine Learning Methods for Multi-Omics Data Integration
Author: Abedalrhman Alkhateeb
language: en
Publisher: Springer Nature
Release Date: 2023-11-13
The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integratingthese large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.
Generative Artificial Intelligence for Biomedical and Smart Health Informatics
Author: Aditya Khamparia
language: en
Publisher: John Wiley & Sons
Release Date: 2025-02-05
Enables readers to understand the future of medical applications with generative AI and related applications Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. Topics covered include: Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disorders Bio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systems Traffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoring Education-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.
Harnessing AI and Machine Learning for Precision Wellness
Advancements in artificial intelligence and machine learning are reshaping healthcare by enabling highly personalized wellness strategies tailored to individual needs. By analyzing vast datasets, including genetic, biometric, and lifestyle information, these technologies can predict disease risks, optimize treatment plans, and recommend proactive health interventions. Precision wellness moves beyond traditional healthcare models, offering dynamic, adaptive solutions that evolve with new scientific discoveries. This shift has the potential to reduce healthcare costs, alleviate the burden on medical systems, and improve overall health outcomes. However, ethical considerations, data privacy, and equitable access remain crucial challenges in realizing the full benefits of AI-driven healthcare. Harnessing AI and Machine Learning for Precision Wellness demystifies the complex world of AI and machine learning in healthcare, exploring how these technologies are being applied across various aspects of wellness. It delves into the mathematical foundations that underpin these technologies, examines real-world case studies, and discusses the ethical considerations that must guide their implementation. This book covers topics such as mathematics, mental health, and preventive care, and is a useful resource for medical and healthcare professionals, computer engineers, data scientists, psychologists, academicians, and researchers.