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Author: Li Zhang
Publisher: Frontiers Media SA
ISBN: 283255055X
Category : Science
Languages : en
Pages : 149
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Book Description
Large databases are created by genomics for the discovery, study, and development of novel treatments all around the world. It's not hard to conceive that artificial intelligence (AI) might currently study the 3 billion base pairs that make up humanoid genetic makeup in order to uncover genetic disparities among the population. By 2026, large pharmaceutical companies hope to have researched up to 2 million genomes and analyzed massive amounts of patient data from clinical drug studies. As new equipment is introduced, AI will be employed in genomics for a variety of omics investigations, including transcriptomics. To aid in the classification of potentially clinically significant genes, AI is used to combine data from genomic research with literature analysis. Machine learning is now a critical component of the genomics industry's growth. AI and Machine learning in genomics is already having an impact on a number of areas, including genetic testing, medical care delivery, and genomics accessibility for people interested in learning more about how their genes influence their health. The purpose of this research is to explore AI and Machine learning applications in gene technology and their roles in paving the way for future genomics machine learning applications.
Author: Li Zhang
Publisher: Frontiers Media SA
ISBN: 283255055X
Category : Science
Languages : en
Pages : 149
Get Book Here
Book Description
Large databases are created by genomics for the discovery, study, and development of novel treatments all around the world. It's not hard to conceive that artificial intelligence (AI) might currently study the 3 billion base pairs that make up humanoid genetic makeup in order to uncover genetic disparities among the population. By 2026, large pharmaceutical companies hope to have researched up to 2 million genomes and analyzed massive amounts of patient data from clinical drug studies. As new equipment is introduced, AI will be employed in genomics for a variety of omics investigations, including transcriptomics. To aid in the classification of potentially clinically significant genes, AI is used to combine data from genomic research with literature analysis. Machine learning is now a critical component of the genomics industry's growth. AI and Machine learning in genomics is already having an impact on a number of areas, including genetic testing, medical care delivery, and genomics accessibility for people interested in learning more about how their genes influence their health. The purpose of this research is to explore AI and Machine learning applications in gene technology and their roles in paving the way for future genomics machine learning applications.
Author: Deepak Kumar Jain
Publisher: Frontiers Media SA
ISBN: 2832521843
Category : Science
Languages : en
Pages : 136
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Book Description
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Author: Michael Mahler
Publisher: Academic Press
ISBN: 032385432X
Category : Science
Languages : en
Pages : 302
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Book Description
Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine
Author: Jason Baron
Publisher: Elsevier Health Sciences
ISBN: 0323939848
Category : Medical
Languages : en
Pages : 161
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Book Description
In this issue, guest editors bring their considerable expertise to this important topic.Provides in-depth reviews on the latest updates in the field, providing actionable insights for clinical practice. Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize
Author: Sanjay Saxena
Publisher: Elsevier
ISBN: 0443185107
Category : Medical
Languages : en
Pages : 360
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Book Description
Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm—Volume 2: Genetics and Clinical Applications provides readers with a broad and detailed framework for radiomics and radiogenomics (R-n-R) approaches with AI in neuro-oncology. It delves into the study of cancer biology and genomics, presenting methods and techniques for analyzing these elements. The book also highlights current solutions that R-n-R can offer for personalized patient treatments, as well as discusses the limitations and future prospects of AI technologies. Volume 1: Radiogenomics Flow Using Artificial Intelligence covers the genomics and molecular study of brain cancer, medical imaging modalities and their analysis in neuro-oncology, and the development of prognostic and predictive models using radiomics. Volume 2: Genetics and Clinical Applications extends the discussion to imaging signatures that correlate with molecular characteristics of brain cancer, clinical applications of R-n-R in neuro-oncology, and the use of Machine Learning and Deep Learning approaches for R-n-R in neuro-oncology. - Includes coverage of foundational concepts of the emerging fields of Radiomics and Radiogenomics - Covers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutations - Presents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosis - Provides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma
Author: Stanley Cohen
Publisher: Elsevier Health Sciences
ISBN: 0323675379
Category : Medical
Languages : en
Pages : 290
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Book Description
Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. - Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. - Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. - Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.
Author: Jyh-An Lee
Publisher: Oxford University Press (UK)
ISBN: 0198870949
Category : Law
Languages : en
Pages : 465
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Book Description
This edited volume provides a broad and comprehensive picture of the intersection between Artificial Intelligence technology and Intellectual Property law, covering business and the basics of AI, the interactions between AI and patent law, copyright law, and IP administration, and the legal aspects of software and data.
Author: Edenilson Brandl
Publisher: Edenilson Brandl
ISBN:
Category : Health & Fitness
Languages : en
Pages : 267
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Book Description
It is with great enthusiasm that I present to you the book "Genomic Biointelligence". This book is a fascinating journey through the ever-evolving world of genomics and artificial intelligence, exploring their intersection and the role of the genomic biointelligence within this context. Genomics has revolutionized our understanding of the genetic code and brought with it a vast volume of data that challenges our ability to analyze and interpret. On the other hand, artificial intelligence has emerged as a powerful tool to deal with this complexity and extract valuable information from genomic data. Within the pages of this book, you will be guided on a comprehensive journey through key topics related to the application of artificial intelligence in genomics. From the history and evolution of artificial intelligence in genomics research to the latest applications in diagnostics, drug discovery, precision medicine and disease research, each chapter presents an important aspect of this rapidly expanding field. You will learn about genetic algorithms and their application in genomics, mathematical modeling of genomic regulatory networks, the use of neural networks in predicting protein structures, and much more. We will also discuss the challenges and limitations of using artificial intelligence in genomics, as well as ethical issues and the importance of data privacy. In addition, we will highlight the fundamental role of the genomic biointelligencist, a multidisciplinary professional who combines knowledge in genomics, artificial intelligence, bioinformatics and other related areas. The genomic biointelligence plays a crucial role in applying artificial intelligence to advance genomic research, discover new treatments, develop personalized therapies, and drive precision medicine. As we progress through this book, you will be invited to explore recent advances and the exciting possibilities that arise from the combination of genomics and artificial intelligence. Through practical examples, case studies and in-depth discussions, we hope to provide you with a solid understanding of the concepts and applications of this rapidly expanding field. Finally, I would like to express my gratitude to all the experts and researchers who contributed their unique knowledge and insights to this book. Their efforts and dedication are instrumental in advancing the field of genomics and artificial intelligence. I hope you will find this book a valuable source of information and inspiration. May it arouse your curiosity, stimulate discussions and motivate you to further explore the frontiers of knowledge in the field of genomics and artificial intelligence.
Author: Khalid Raza
Publisher: Elsevier
ISBN: 0443275750
Category : Science
Languages : en
Pages : 478
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Book Description
Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications, the intersection of deep learning and genetics opens up new avenues for advancing our understanding of the genetic code, gene regulation, and the broader genomics landscape. The book not only covers the most up-to-date advancements in the field of deep learning in genetics and genomics, but also a wide spectrum of (sub) topics including medical and clinical genetics, predictive medicine, transcriptomic, and gene expression studies. In 21 chapters Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications describes how AI and DL have become increasingly useful in genetics and genomics research where both play a crucial role by accelerating research, improving the understanding of the human genome, and enabling personalized healthcare. From the fundamentals concepts and practical applications of deep learning algorithms to a wide range of challenging problems from genetics and genomics, Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications creates a better knowledge of the biological and genetics mechanisms behind disease illnesses and improves the forecasting abilities using the different methodologies described. This title offers a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning-based approaches in genetics and genomics, helping researchers process and interpret vast amounts of genetic data, identify patterns, and make discoveries that would be challenging or impossible using traditional methods. - Brings together fundamental concepts of genetics, genomics, and deep learning - Includes how to build background of solution methodologies and design of mathematical and logical algorithms - Delves into the intersection of deep learning and genetics, offering a comprehensive exploration of how deep learning techniques can be applied to various aspects of genomics
