Hongyu Zhao – författare

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.

Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.

Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.

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.

Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.