Genomics At The Nexus Of Ai Computer Vision And Machine Learning
Download Genomics At The Nexus Of Ai Computer Vision And Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Genomics At The Nexus Of Ai Computer Vision And Machine Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Genomics at the Nexus of AI, Computer Vision, and Machine Learning
Author: Shilpa Choudhary
language: en
Publisher: John Wiley & Sons
Release Date: 2024-10-01
The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies.
Revolutionizing Academic Research With AI and Augmented Reality
Artificial intelligence (AI) and augmented reality (AR) have redefined how researchers discover knowledge and how they analyzed and shared. By using AI’s powerful data processing capabilities and AR’s immersive tools, researchers can explore complex theories and massive datasets. This fusion is not just enhancing existing methodologies, it’s revolutionizing the very fabric of scholarly inquiry, paving the way for more dynamic, intuitive, and impactful research outcomes. Revolutionizing Academic Research With AI and Augmented Reality explores how universities can navigate the technological advancements of AI and AR in research and education. This book utilizes case studies to inspire educators and administrators to rethink how to use technological advancements with the new academic paradigms. Covering topics such as academic integrity, scholarly communication, and virtual labs, this book is an excellent resource for educators, researchers, university administrators, policymakers, students, academicians, and more.
Machine Learning for Neurodegenerative Disorders
This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.