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Machine Learning in Genome-Wide Association Studies

Machine Learning in Genome-Wide Association Studies PDF Author: Ting Hu
Publisher: Frontiers Media SA
ISBN: 2889662292
Category : Science
Languages : en
Pages : 74

Book Description
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.

Machine Learning in Genome-Wide Association Studies

Machine Learning in Genome-Wide Association Studies PDF Author: Ting Hu
Publisher: Frontiers Media SA
ISBN: 2889662292
Category : Science
Languages : en
Pages : 74

Book Description
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.

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics PDF Author: Elena Marchiori
Publisher: Springer Science & Business Media
ISBN: 354071782X
Category : Computers
Languages : en
Pages : 311

Book Description
This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.

Artificial Intelligence

Artificial Intelligence PDF Author:
Publisher: BoD – Books on Demand
ISBN: 1789840171
Category : Medical
Languages : en
Pages : 142

Book Description
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.

Multivariate Statistical Machine Learning Methods for Genomic Prediction

Multivariate Statistical Machine Learning Methods for Genomic Prediction PDF Author: Osval Antonio Montesinos López
Publisher: Springer Nature
ISBN: 3030890104
Category : Technology & Engineering
Languages : en
Pages : 707

Book Description
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.

Machine Learning for Health Informatics

Machine Learning for Health Informatics PDF Author: Andreas Holzinger
Publisher: Springer
ISBN: 3319504789
Category : Computers
Languages : en
Pages : 481

Book Description
Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

Handbook of Machine Learning Applications for Genomics

Handbook of Machine Learning Applications for Genomics PDF Author: Sanjiban Sekhar Roy
Publisher: Springer Nature
ISBN: 9811691584
Category : Technology & Engineering
Languages : en
Pages : 222

Book Description
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.

Genetic Dissection of Complex Traits

Genetic Dissection of Complex Traits PDF Author: D.C. Rao
Publisher: Academic Press
ISBN: 9780080569116
Category : Science
Languages : en
Pages : 300

Book Description
The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. * Five sections on the latest advances in complex traits * Methods for testing with ethical, legal, and social implications * Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more

Statistical Methods, Computing, and Resources for Genome-Wide Association Studies

Statistical Methods, Computing, and Resources for Genome-Wide Association Studies PDF Author: Riyan Cheng
Publisher: Frontiers Media SA
ISBN: 2889712125
Category : Science
Languages : en
Pages : 148

Book Description


Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics PDF Author: Daniel Sorensen
Publisher: Springer Science & Business Media
ISBN: 0387954406
Category : Science
Languages : en
Pages : 745

Book Description
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.

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning PDF Author: Rani, Geeta
Publisher: IGI Global
ISBN: 1799827437
Category : Medical
Languages : en
Pages : 586

Book Description
By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.