Shili Lin – författare

The past three decades have witnessed an explosion of what is now referred to as high-dimensional `omics'' data. Bioinformatics Methods: From Omics to Next Generation Sequencing describes the statistical methods and analytic frameworks that are best equipped to interpret these complex data and how they apply to health-related research. Covering the technologies that generate data, subtleties of various data types, and statistical underpinnings of methods, this book identifies a suite of potential analytic tools, and highlights commonalities among statistical methods that have been developed.

An ideal reference for biostatisticians and data analysts that work in collaboration with scientists and clinical investigators looking to ensure rigorous application of available methodologies.

Key Features:

Survey of a variety of omics data types and their unique features Summary of statistical underpinnings for widely used omics data analysis methods Description of software resources for performing omics data analyses

The past three decades have witnessed an explosion of what is now referred to as high-dimensional `omics' data. Bioinformatics Methods: From Omics to Next Generation Sequencing describes the statistical methods and analytic frameworks that are best equipped to interpret these complex data and how they apply to health-related research. Covering the technologies that generate data, subtleties of various data types, and statistical underpinnings of methods, this book identifies a suite of potential analytic tools, and highlights commonalities among statistical methods that have been developed.An ideal reference for biostatisticians and data analysts that work in collaboration with scientists and clinical investigators looking to ensure rigorous application of available methodologies.Key Features:Survey of a variety of omics data types and their unique featuresSummary of statistical underpinnings for widely used omics data analysis methodsDescription of software resources for performing omics data analyses

The past three decades have witnessed an explosion of what is now referred to as high-dimensional `omics'' data. Bioinformatics Methods: From Omics to Next Generation Sequencing describes the statistical methods and analytic frameworks that are best equipped to interpret these complex data and how they apply to health-related research. Covering the technologies that generate data, subtleties of various data types, and statistical underpinnings of methods, this book identifies a suite of potential analytic tools, and highlights commonalities among statistical methods that have been developed.

An ideal reference for biostatisticians and data analysts that work in collaboration with scientists and clinical investigators looking to ensure rigorous application of available methodologies.

Key Features:

Survey of a variety of omics data types and their unique features Summary of statistical underpinnings for widely used omics data analysis methods Description of software resources for performing omics data analyses

The discipline of statistical genetics is highly computational. Be it exact compu- tionalmethods,simulationbased,orahybridofthetwo,computationalpackagesare indispensable tools and constant companions of researchers in the ?eld. This ha- bookisintendedtoprovidehumangeneticistsandotherbiomedicalresearcherswith guidance on selections of appropriate computational methods and software pa- ages for their speci?c genetic problems. It may also be used by students and other learners as a reference in conjunction with a more theoretical and/or methodolo- cally oriented text book. This book tries to strike a balance between methodological expositions and practical guidelines for software selections. Wherever possible, comparisons among the competing methods and software are made to highlight the relativeadvantagesanddisadvantagesoftheapproaches,so thatthe readercanmake informed choices to best match their speci?c needs. Human genetics has been undergoing an evolution in the past several years as new knowledge and technologies are transforming the ?eld, leading to numerous new discoveriesof genes associated with complex traits such as cancer, obesity, and diabetes. Many recent genome-wide association studies employ the case–control design, where the study subjects consist of unrelated affected individuals and n- mal controls. For each individual, a large number of genetic markers are queried.

The discipline of statistical genetics is highly computational. Be it exact compu- tionalmethods,simulationbased,orahybridofthetwo,computationalpackagesare indispensable tools and constant companions of researchers in the ?eld. This ha- bookisintendedtoprovidehumangeneticistsandotherbiomedicalresearcherswith guidance on selections of appropriate computational methods and software pa- ages for their speci?c genetic problems. It may also be used by students and other learners as a reference in conjunction with a more theoretical and/or methodolo- cally oriented text book. This book tries to strike a balance between methodological expositions and practical guidelines for software selections. Wherever possible, comparisons among the competing methods and software are made to highlight the relativeadvantagesanddisadvantagesoftheapproaches,so thatthe readercanmake informed choices to best match their speci?c needs. Human genetics has been undergoing an evolution in the past several years as new knowledge and technologies are transforming the ?eld, leading to numerous new discoveriesof genes associated with complex traits such as cancer, obesity, and diabetes. Many recent genome-wide association studies employ the case–control design, where the study subjects consist of unrelated affected individuals and n- mal controls. For each individual, a large number of genetic markers are queried.