Deep Learning For 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. 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 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 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
Deep Learning For Genomics
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Author : Upendra Kumar Devisetty
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-11-11
Deep Learning For Genomics written by Upendra Kumar Devisetty and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-11 with Computers categories.
Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries Key FeaturesApply deep learning algorithms to solve real-world problems in the field of genomicsExtract biological insights from deep learning models built from genomic datasetsTrain, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomicsBook Description Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. What you will learnDiscover the machine learning applications for genomicsExplore deep learning concepts and methodologies for genomics applicationsUnderstand supervised deep learning algorithms for genomics applicationsGet to grips with unsupervised deep learning with autoencodersImprove deep learning models using generative modelsOperationalize deep learning models from genomics datasetsVisualize and interpret deep learning modelsUnderstand deep learning challenges, pitfalls, and best practicesWho this book is for This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.
Applications Of Deep Learning In Genomics
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Author : Bipin Kumar Rai
language : en
Publisher: CRC Press
Release Date : 2025-11-12
Applications Of Deep Learning In Genomics written by Bipin Kumar Rai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-12 with Science categories.
The objective of the book is to use advanced deep learning techniques to unlock the complexities of genomic data. It reveals hidden and unknown patterns, to improve our understanding the role of genetics in diseases, speeding up drug discovery processes, illuminating evolutionary trajectories, and dealing with the challenges posed by large genomic datasets. It also addresses ethical concerns, provides real-world applications, reviews future frontiers like as quantum computing and multi-omics integration, and presents a thorough picture of the genetic environment. Overall, the book aims to enhance our ability to use genomics for more precise forecasts, personalised therapies, and a better understanding of the underlying genetic fabric that defines life.
Handbook Of Machine Learning Applications For Genomics
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Author : Sanjiban Sekhar Roy
language : en
Publisher: Springer Nature
Release Date : 2022-06-23
Handbook Of Machine Learning Applications For Genomics written by Sanjiban Sekhar Roy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-23 with Technology & Engineering categories.
Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.
Machine Learning And Systems Biology In Genomics And Health
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Author : Shailza Singh
language : en
Publisher: Springer Nature
Release Date : 2022-02-04
Machine Learning And Systems Biology In Genomics And Health written by Shailza Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-04 with Science categories.
This book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.
Deep Learning In Genome Mapping
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Author : Soumya Ranjan Nayak
language : en
Publisher: CRC Press
Release Date : 2026-03-05
Deep Learning In Genome Mapping written by Soumya Ranjan Nayak and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-03-05 with Computers categories.
The book aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field. Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.
Applications Of Deep Learning In Genomics
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Author : Bipin Kumar Rai
language : en
Publisher:
Release Date : 2025
Applications Of Deep Learning In Genomics written by Bipin Kumar Rai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with Deep learning (Machine learning) categories.
"The objective of "Deep Learning's Expedition into Genomic Revelations" is to use advanced deep learning techniques to unlock the complexities of genomic data, with the goal of revealing hidden patterns, improving our understanding of genetic contributions to diseases, speeding up drug discovery processes, illuminating evolutionary trajectories, and dealing with the challenges posed by large genomic datasets. This expedition aims to address ethical concerns, demonstrate real-world applications, investigate future frontiers like as quantum computing and multi-omics integration, and present a thorough picture of the genetic environment. The main objective is to enhance genomics, allowing for more precise forecasts, personalised therapies, and a better understanding of the underlying genetic fabric that defines life"-- Provided by publisher.
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.