Deep Learning For Genome Wide Association Studies
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Deep Learning For Genome Wide Association Studies
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Author : Deepak Sharma
language : en
Publisher:
Release Date : 2022
Deep Learning For Genome Wide Association Studies written by Deepak Sharma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
"Genome-Wide Association Studies (GWAS) are a popular tool in statistical genomics that are used to identify genetic variants associated with various dis- eases. However, their success has been limited, in part because they typically do not incorporate interactions between variants to model target traits. Since Deep neural networks have been successful across domains abundant with com- plex signals, like speech, language, and vision, they are also popular candidates for modelling interactions between genetic variants. However, their black-box nature is a hindrance to their application for GWAS. In this thesis, we present a pipeline to train and interpret feedforward neu- ral networks to conduct a genome-wide association study (GWAS). We show that trained deep neural networks can be interpreted using feature-importance techniques to accurately distinguish and rank simulated causal genetic variants. We improve its accuracy by extending the pipeline to the multi-task setting, wherein we simultaneously model two related, simulated traits. We demon- strate the accuracy, reliability, and scalability of our approach by identifying most known Diabetes genetic risk factors found using a conventional GWAS on the UK Biobank"--
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.
Deep Learning For Genome Wide Association Studies And The Impact Of Snp Locations
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Author : Songyuan Ji
language : en
Publisher:
Release Date : 2019
Deep Learning For Genome Wide Association Studies And The Impact Of Snp Locations written by Songyuan Ji and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
The study of Single Nucleotide Polymorphisms (SNPs) associated with human diseases is important for identifying pathogenic genetic variants and illuminating the genetic architecture of complex diseases. A Genome-wide association study (GWAS) examines genetic variation in different individuals and detects disease related SNPs. The traditional machine learning methods always use SNPs data as a sequence to analyze and process and thus may overlook the complex interacting relationships among multiple genetic factors. In this thesis, we propose a new hybrid deep learning approach to identify susceptibility SNPs associated with colorectal cancer. A set of SNPs variants were first selected by a hybrid feature selection algorithm, and then organized as 3D images using a selection of space-filling curve models. A multi-layer deep Convolutional Neural Network was constructed and trained using those images. We found that images generated using the space-filling curve model that preserve the original SNP locations in the genome yield the best classification performance. We also report a set of high risk SNPs associate with colorectal cancer as the result of the deep neural network model.
Artificial Intelligence
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2019-07-31
Artificial Intelligence written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-31 with Medical categories.
Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area.
Genome Wide Association Studies And Genomic Selection For Crop Improvement In The Era Of Big Data
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Author : Nunzio D’Agostino
language : en
Publisher: Frontiers Media SA
Release Date : 2023-05-05
Genome Wide Association Studies And Genomic Selection For Crop Improvement In The Era Of Big Data written by Nunzio D’Agostino 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 2023-05-05 with Science categories.
Likelihood Bayesian And Mcmc Methods In Quantitative Genetics
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Author : Daniel Sorensen
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-03-22
Likelihood Bayesian And Mcmc Methods In Quantitative Genetics written by Daniel Sorensen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-22 with Science categories.
This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.
Deep Learning In Biology And Medicine
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Author : Davide Bacciu
language : en
Publisher: World Scientific
Release Date : 2022-01-17
Deep Learning In Biology And Medicine written by Davide Bacciu and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-17 with Computers categories.
Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.
Machine Learning Advanced Dynamic Omics Data Analysis For Precision Medicine
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Author : Tao Zeng
language : en
Publisher: Frontiers Media SA
Release Date : 2020-03-30
Machine Learning Advanced Dynamic Omics Data Analysis For Precision Medicine written by Tao Zeng 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 2020-03-30 with categories.
Advances In Genetics
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Author :
language : en
Publisher: Academic Press
Release Date : 2019-06-06
Advances In Genetics written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-06 with Science categories.
Advances in Genetics, Volume 104, provides the latest information on the rapidly evolving field of genetics, presenting new medical breakthroughs that are occurring as a result of advances in our knowledge of the topic. The book continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines, critically analyzing future directions. - Critically analyzes future directions for the study of clinical genetics - Written and edited by recognized leaders in the field - Presents new medical breakthroughs that are occurring as a result of advances in our knowledge of genetics
Machine Learning And Artificial Intelligence In Radiation Oncology
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Author : Barry S. Rosenstein
language : en
Publisher: Academic Press
Release Date : 2023-12-02
Machine Learning And Artificial Intelligence In Radiation Oncology written by Barry S. Rosenstein and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-02 with Science categories.
Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. - Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic - Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations - Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic