Statistical Methods For The Analysis Of Genomic Data

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Statistical Methods For The Analysis Of Genomic Data

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Author : Hui Jiang
language : en
Publisher: MDPI
Release Date : 2020-12-29

Statistical Methods For The Analysis Of Genomic Data written by Hui Jiang and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-29 with Science categories.

In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

Statistical Methods For The Analysis Of Genomic Data

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Author : Hui Jiang
language : en
Publisher:
Release Date : 2020

Statistical Methods For The Analysis Of Genomic Data written by Hui Jiang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

Computational Methods For The Analysis Of Genomic Data And Biological Processes

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Author : Francisco A. Gómez Vela
language : en
Publisher: MDPI
Release Date : 2021-02-05

Computational Methods For The Analysis Of Genomic Data And Biological Processes written by Francisco A. Gómez Vela and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-05 with Medical categories.

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.

Statistical Methods Computing And Resources For Genome Wide Association Studies

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Author : Riyan Cheng
language : en
Publisher: Frontiers Media SA
Release Date : 2021-08-24

Statistical Methods Computing And Resources For Genome Wide Association Studies written by Riyan Cheng 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 2021-08-24 with Science categories.

Big Data Analytics In Genomics

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Author : Ka-Chun Wong
language : en
Publisher: Springer
Release Date : 2016-10-24

Big Data Analytics In Genomics written by Ka-Chun Wong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-24 with Computers categories.

This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.

Essential Concepts In Molecular Pathology

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Author : William B. Coleman
language : en
Publisher: Academic Press
Release Date : 2019-11-23

Essential Concepts In Molecular Pathology written by William B. Coleman and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-23 with Medical categories.

Essential Concepts in Molecular Pathology, Second Edition, offers an introduction to molecular genetics and the "molecular" aspects of human disease. The book illustrates how pathologists harness their understanding of these entities to develop new diagnostics and treatments for various human diseases. This new edition offers pathology, genetics residents, and molecular pathology fellows an advanced understanding of the molecular mechanisms of disease that goes beyond what they learned in medical and graduate school. By bridging molecular concepts of pathogenesis to the clinical expression of disease in cell, tissue and organ, this fully updated, introductory reference provides the background necessary for an understanding of today's advances in pathology and medicine. - Explains the practice of "molecular medicine" and the translational aspects of molecular pathology, including molecular diagnostics, molecular assessment and personalized medicine - Orients non-pathologists on what pathologists look for and how they interpret their observational findings based on histopathology - Provides the reader with what is missing from most targeted introductions to pathology—the cell biology behind pathophysiology

Understanding Applying Basic Statistical Methods Using R

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Author : Morgan Holland &
language : en
Publisher: Scientific e-Resources
Release Date : 2019-07-04

Understanding Applying Basic Statistical Methods Using R written by Morgan Holland & and has been published by Scientific e-Resources this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-04 with categories.

Understanding and Applying Basic Statistical Methods Using R remarkably conquers any hindrance between propels in the measurable writing and methods routinely utilized by non-analysts. Giving a theoretical premise to understanding the relative benefits and uses of these methods, the book highlights current bits of knowledge and advances applicable to fundamental systems regarding managing non-ordinariness, exceptions, heteroscedasticity (unequal changes), and curvature. Including a manual for R, the book utilizes R programming to investigate starting factual ideas and standard methods for managing known issues related with exemplary procedures. Altogether classroom tried, the book incorporates segments that attention on either R programming or computational points of interest to enable the reader to wind up noticeably familiar with fundamental ideas and standards basic regarding understanding and applying the numerous methods as of now accessible.

Principles And Methods For Data Science

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Author :
language : en
Publisher: Elsevier
Release Date : 2020-05-28

Principles And Methods For Data Science written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-28 with Mathematics categories.

Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Statistics series - Updated release includes the latest information on Principles and Methods for Data Science

Analysis Of Microarray Data

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Author : Matthias Dehmer
language : en
Publisher: John Wiley & Sons
Release Date : 2008-03-17

Analysis Of Microarray Data written by Matthias Dehmer 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 2008-03-17 with Medical categories.

This book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents: * Understanding and Preprocessing Microarray Data * Clustering of Microarray Data * Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order * Bilayer Verification Algorithm * Probabilistic Boolean Networks as Models for Gene Regulation * Estimating Transcriptional Regulatory Networks by a Bayesian Network * Analysis of Therapeutic Compound Effects * Statistical Methods for Inference of Genetic Networks and Regulatory Modules * Identification of Genetic Networks by Structural Equations * Predicting Functional Modules Using Microarray and Protein Interaction Data * Integrating Results from Literature Mining and Microarray Experiments to Infer Gene Networks The book is for both, scientists using the technique as well as those developing new analysis techniques.

Statistical Methods For The Analysis Of Genomic Data From Tiling Arrays And Next Generation Sequencing Technologies

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Author : Pei Fen Kuan
language : en
Publisher:
Release Date : 2009

Statistical Methods For The Analysis Of Genomic Data From Tiling Arrays And Next Generation Sequencing Technologies written by Pei Fen Kuan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.