Deep Learning In Genetics And Genomics
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Deep Learning In Genetics And Genomics
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Author : Khalid Raza
language : en
Publisher: Elsevier
Release Date : 2024-11-28
Deep Learning In Genetics And Genomics written by Khalid Raza and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-28 with Science categories.
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
Deep Learning In Genetics And Genomics
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Author : Khalid Raza
language : en
Publisher: Elsevier
Release Date : 2024-11-28
Deep Learning In Genetics And Genomics written by Khalid Raza and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-28 with Science categories.
Deep Learning in Genetics and Genomics: Vol. 2 (Advanced Applications) delves into the Deep Learning methods and their applications in various fields of studies, including genetics and genomics, bioinformatics, health informatics and medical informatics generating the momentum of today's developments in the field. In 25 chapters this title covers advanced applications in the field which includes deep learning in predictive medicines), analysis of genetic and clinical features, transcriptomics and gene expression patterns analysis, clinical decision support in genetic diagnostics, deep learning in personalised genomics and gene editing, and understanding genetic discoveries through Explainable AI. Further, it also covers various deep learning-based case studies, making this book a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning based approaches. This volume is not only a valuable resource for health educators, clinicians, and healthcare professionals but also to graduate students of genetics, genomics, biology, biostatistics, biomedical sciences, bioinformatics, and interdisciplinary sciences. - Embraces the potential that deep learning holds for understanding genome biology - Encourages further advances in this area, extending to all aspects of genomics research - Provides Deep Learning algorithms in genetic and genomic research
Deep Learning For The Life Sciences
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Author : Bharath Ramsundar
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-04-10
Deep Learning For The Life Sciences written by Bharath Ramsundar and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-10 with Science categories.
Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
Multivariate Statistical Machine Learning Methods For Genomic Prediction
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Author : Osval Antonio Montesinos López
language : en
Publisher: Springer Nature
Release Date : 2022-02-14
Multivariate Statistical Machine Learning Methods For Genomic Prediction written by Osval Antonio Montesinos López and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Technology & Engineering categories.
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Proceedings Of Fifth Doctoral Symposium On Computational Intelligence
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Author : Abhishek Swaroop
language : en
Publisher: Springer Nature
Release Date : 2024-11-29
Proceedings Of Fifth Doctoral Symposium On Computational Intelligence written by Abhishek Swaroop and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.
This book features high-quality research papers presented at Fifth Doctoral Symposium on Computational Intelligence (DoSCI 2024), jointly organised by Institute of Engineering & Technology, Lucknow, India, and School of Open Learning, University of Delhi in association with University of Calabria, Italy, on May 10, 2024. This book discusses the topics such as computational intelligence, artificial intelligence, deep learning, evolutionary algorithms, swarm intelligence, fuzzy sets and vague sets, rough set theoretic approaches, quantum-inspired computational intelligence, hybrid computational intelligence, machine learning, computer vision, soft computing, distributed computing, parallel and grid computing, cloud computing, high-performance computing, biomedical computing, and decision support and decision making.
Machine Learning In Genome Wide Association Studies
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Author : Ting Hu
language : en
Publisher: Frontiers Media SA
Release Date : 2020-12-15
Machine Learning In Genome Wide Association Studies written by Ting Hu and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-15 with Science categories.
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Genomics At The Nexus Of Ai Computer Vision And Machine Learning
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Author : Shilpa Choudhary
language : en
Publisher: John Wiley & Sons
Release Date : 2024-11-05
Genomics At The Nexus Of Ai Computer Vision And Machine Learning written by Shilpa Choudhary and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-05 with Computers categories.
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.
Advances In Molecular Pathology 2024
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Author : Ahmad Abou Tayoun
language : en
Publisher: Elsevier Health Sciences
Release Date : 2024-10-05
Advances In Molecular Pathology 2024 written by Ahmad Abou Tayoun and has been published by Elsevier Health Sciences this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-05 with Medical categories.
Advances in Molecular Pathology reviews the year's most important findings and updates within the field in order to provide practitioners with the current clinical information they need to improve patient outcomes. A distinguished editorial board identifies key areas of major progress and controversy and invites preeminent specialists to contribute original articles devoted to these topics. These insightful overviews in molecular pathology inform and enhance clinical practice by bringing concepts to a clinical level and exploring their everyday impact on patient care. - Contains 24 articles on such topics as chronic myelomonocytic leukemia; antimicrobial resistance marker detection and clinical interpretation/limitations; DPYD and the congenital DPD deficiency; FHIR genomic operations; AI in genomics; clinical value of molecular LDTs; and more. - Provides in-depth, clinical reviews in molecular pathology, providing actionable insights for clinical practice. - Presents the latest information in the field under the leadership of an experienced editorial team. Authors synthesize and distill the latest research and practice guidelines to create these timely topic-based reviews.
Applying Machine Learning Techniques To Bioinformatics Few Shot And Zero Shot Methods
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Author : Lilhore, Umesh Kumar
language : en
Publisher: IGI Global
Release Date : 2024-03-22
Applying Machine Learning Techniques To Bioinformatics Few Shot And Zero Shot Methods written by Lilhore, Umesh Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Computers categories.
Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.
Challenges In Information Communication And Computing Technology
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Author : V. Sharmila
language : en
Publisher: CRC Press
Release Date : 2024-12-10
Challenges In Information Communication And Computing Technology written by V. Sharmila and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-10 with Technology & Engineering categories.
This book explores the critical challenges and emerging trends in Information, Communication, and Computing Technology (ICCT). It provides a comprehensive overview of the key issues facing these rapidly evolving fields, from data security and privacy to advancements in artificial intelligence, communication networks, and quantum computing. Through in-depth analysis and expert perspectives, this volume aims to shed light on the complexities of ICCT and offer innovative solutions for researchers, practitioners, and students. Building on its exploration of challenges in ICCT, this book delves into several core areas. These include the development and deployment of secure and efficient communication networks, the ethical implications and technical hurdles of artificial intelligence and machine learning, and the promise and complexity of quantum computing. The book also addresses the management of big data, highlighting both its potential and the challenges of ensuring data privacy and security. Additionally, it examines the role of sustainability in computing, advocating for greener technologies and practices. The findings presented in this volume emphasize the need for interdisciplinary approaches and innovative thinking to address these challenges, offering insights that are both practical and forward-looking. This book is intended for a diverse audience that includes researchers, practitioners, and students in the fields of Information, Communication, and Computing Technology (ICCT). It is particularly valuable for academics and professionals seeking to deepen their understanding of current challenges and emerging trends in these areas. Additionally, policymakers, industry leaders, and technologists will find the book's insights useful for informing decisions and strategies in the development and implementation of advanced technologies. Whether you are a seasoned expert or a newcomer to the field, this book provides valuable perspectives that can enhance your knowledge and contribute to your work in ICCT. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND)] 4.0 license.