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Deep generative models for integrative analysis of Alzheimer's biomarkers

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep generative models for integrative analysis of Alzheimer's biomarkers/ edited by Abhishek Kumar, Rakesh Sakthivel, Gayathri Nagasubramanian, Srivel Ravi, Dhaya Chinnathambi.
other author:	Kumar, Abhishek,
Published:	Hershey, Pennsylvania :IGI Global, : 2025.,
Description:	1 online resource (xxiv, 510 p.) :ill. :
Subject:	Alzheimer's disease - Research. -
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-6442-0
ISBN:	9798369364444

Deep generative models for integrative analysis of Alzheimer's biomarkers
Deep generative models for integrative analysis of Alzheimer's biomarkers[electronic resource] /edited by Abhishek Kumar, Rakesh Sakthivel, Gayathri Nagasubramanian, Srivel Ravi, Dhaya Chinnathambi. - Hershey, Pennsylvania :IGI Global,2025. - 1 online resource (xxiv, 510 p.) :ill. - Advances in psychology, mental health, and behavioral studies (APMHBS) book series. - Advances in psychology, mental health, and behavioral studies (APMHBS) book series..

Includes bibliographical references and index.

Preface -- Chapter 1. Bridging the Gap: Integrating Machine Learning With Biomarkers for Enhanced Alzheimer's Detection and Tracking -- Chapter 2. Decoding Alzheimer's AI-Powered Biomarker Analysis for Diagnosis and Monitoring -- Chapter 3. Biomarkers for Alzheimer's Disease: Early Diagnosis -- Chapter 4. Introduction to Alzheimer's Disease and Biomarkers -- Chapter 5. Integrating Machine Learning in Biological Markers for Enhanced Early Detection of Alzheimer's Disease -- Chapter 6. Natural Language Processing of Electronic Health Records for Predicting Alzheimer's Disease -- Chapter 7. Big Data Analytics: NeuroDetect - AI-Driven Big Data Analytics for Alzheimer's Disease -- Chapter 8. Orchestrating Precision in Alzheimer's Disease Progression Forecasting: A Convergence of XGBoost and Random Forest Ensemble -- Chapter 9. Predictive Precision Harnessing AI for Early Alzheimer's Detection -- Chapter 10. Advancing Alzheimer's Disease Detection With Big Data and Machine Learning -- Chapter 11. Harnessing Big Data for Early Detection and Progression Tracking of Alzheimer's Disease -- Chapter 12. Unveiling Alzheimer's: Exploring Biomarkers for Diagnosis and Progression -- Chapter 13. Novel Approaches for Feature Extraction and Representation Learning of Alzheimer's Biomarkers -- Chapter 14. Case Studies and Real-World Application of Deep Generative Models in Alzheimer's Research -- Chapter 15. Deep Generative Models Insights and Applications -- Chapter 16. Real-World Applications and Case Studies of Deep Generative Models in Alzheimer's Disease Research -- Chapter 17. The Role of Biomarkers in Alzheimer's Disease Progression -- Chapter 18. Alzheimer's Disease Prediction Using InceptionResNet Integrating Deep Learning Models -- Compilation of References -- About the Contributors -- Index.

"The integration of generative AI and deep learning techniques for Alzheimer's disease detection significantly impacts the research community by advancing diagnostic accuracy and providing a comprehensive understanding of the disease. By combining multiple data modalities, including imaging, genetics, and clinical data, researchers can improve diagnostic precision and develop personalized treatment strategies. Generative AI facilitates efficient data utilization through dataset augmentation, fostering innovation and collaboration across interdisciplinary fields. These methodologies forward the exploration of new diagnostic tools while expediting their application in clinical practice, benefiting patients through early detection and intervention. The incorporation of generative AI may enhance research capabilities, promote collaboration, and improve Alzheimer's disease management and patient outcomes.Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers explores the integration of deep generative models in disease diagnosis, biomarking, and prediction. It examines the use of tools like data analysis, natural language processing, and machine learning for effective Alzheimer's research. This book covers topics such as data analysis, biomedicine, and machine learning, and is a useful resource for computer engineers, biologists, scientists, medical professionals, healthcare workers, academicians, and researchers."--

Mode of access: World Wide Web.

ISBN: 9798369364444Subjects--Topical Terms:

677734
Alzheimer's disease
--Research.Subjects--Index Terms:

Alzheimer's Disease.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: RC523 / .D44 2025eb

Dewey Class. No.: 616.8/311

National Library of Medicine Call No.: WT 155 / .D44 2025eb

Deep generative models for integrative analysis of Alzheimer's biomarkers
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