國立高雄大學圖資館 |

登入

回首頁

Statistical learning in geneticsan introduction using R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical learning in geneticsby Daniel Sorensen.
其他題名:	an introduction using R /
作者:	Sorensen, Daniel.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xvi, 693 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	GeneticsStatistical methods.
電子資源:	https://doi.org/10.1007/978-3-031-35851-7
ISBN:	9783031358517$q(electronic bk.)

Statistical learning in geneticsan introduction using R /
Sorensen, Daniel.

Statistical learning in geneticsan introduction using R /[electronic resource] :by Daniel Sorensen. - Cham :Springer International Publishing :2023. - xvi, 693 p. :ill., digital ;24 cm. - Statistics for biology and health,2197-5671. - Statistics for biology and health..

This book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology have contributed to the emergence of vast data sets, where millions of genetic markers for each individual are coupled with medical records, generating an unparalleled resource for linking human genetic variation to human biology and disease. Similar developments have taken place in animal and plant breeding, where genetic marker information is combined with production traits. An important task for the statistical geneticist is to adapt, construct and implement models that can extract information from these large-scale data. An initial step is to understand the methodology that underlies the probability models and to learn the modern computer-intensive methods required for fitting these models. The objective of this book, suitable for readers who wish to develop analytic skills to perform genomic research, is to provide guidance to take this first step. This book is addressed to numerate biologists who typically lack the formal mathematical background of the professional statistician. For this reason, considerably more detail in explanations and derivations is offered. It is written in a concise style and examples are used profusely. A large proportion of the examples involve programming with the open-source package R. The R code needed to solve the exercises is provided. The MarkDown interface allows the students to implement the code on their own computer, contributing to a better understanding of the underlying theory. Part I presents methods of inference based on likelihood and Bayesian methods, including computational techniques for fitting likelihood and Bayesian models. Part II discusses prediction for continuous and binary data using both frequentist and Bayesian approaches. Some of the models used for prediction are also used for gene discovery. The challenge is to find promising genes without incurring a large proportion of false positive results. Therefore, Part II includes a detour on False Discovery Rate assuming frequentist and Bayesian perspectives. The last chapter of Part II provides an overview of a selected number of non-parametric methods. Part III consists of exercises and their solutions. Daniel Sorensen holds PhD and DSc degrees from the University of Edinburgh and is an elected Fellow of the American Statistical Association. He was professor of Statistical Genetics at Aarhus University where, at present, he is professor emeritus.

ISBN: 9783031358517$q(electronic bk.)

Standard No.: 10.1007/978-3-031-35851-7doiSubjects--Topical Terms:

186254
Genetics
--Statistical methods.

LC Class. No.: QH438.4.S73

Dewey Class. No.: 576.5015195

Statistical learning in geneticsan introduction using R /
LDR:03578nmm a2200325 a 4500 001 651478
003 DE-He213
005 20230919222828.0
006 m d
007 cr nn 008maaau
008 240327s2023 sz s 0 eng d
020 $a 9783031358517$q(electronic bk.)
020 $a 9783031358500$q(paper)
024 7 $a 10.1007/978-3-031-35851-7 $2 doi
035 $a 978-3-031-35851-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QH438.4.S73
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 576.5015195 $2 23
090 $a QH438.4.S73 $b S713 2023
100 1 $a Sorensen, Daniel. $3 240037
245 1 0 $a Statistical learning in genetics $h [electronic resource] : $b an introduction using R / $c by Daniel Sorensen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xvi, 693 p. : $b ill., digital ; $c 24 cm.
490 1 $a Statistics for biology and health, $x 2197-5671
520 $a This book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology have contributed to the emergence of vast data sets, where millions of genetic markers for each individual are coupled with medical records, generating an unparalleled resource for linking human genetic variation to human biology and disease. Similar developments have taken place in animal and plant breeding, where genetic marker information is combined with production traits. An important task for the statistical geneticist is to adapt, construct and implement models that can extract information from these large-scale data. An initial step is to understand the methodology that underlies the probability models and to learn the modern computer-intensive methods required for fitting these models. The objective of this book, suitable for readers who wish to develop analytic skills to perform genomic research, is to provide guidance to take this first step. This book is addressed to numerate biologists who typically lack the formal mathematical background of the professional statistician. For this reason, considerably more detail in explanations and derivations is offered. It is written in a concise style and examples are used profusely. A large proportion of the examples involve programming with the open-source package R. The R code needed to solve the exercises is provided. The MarkDown interface allows the students to implement the code on their own computer, contributing to a better understanding of the underlying theory. Part I presents methods of inference based on likelihood and Bayesian methods, including computational techniques for fitting likelihood and Bayesian models. Part II discusses prediction for continuous and binary data using both frequentist and Bayesian approaches. Some of the models used for prediction are also used for gene discovery. The challenge is to find promising genes without incurring a large proportion of false positive results. Therefore, Part II includes a detour on False Discovery Rate assuming frequentist and Bayesian perspectives. The last chapter of Part II provides an overview of a selected number of non-parametric methods. Part III consists of exercises and their solutions. Daniel Sorensen holds PhD and DSc degrees from the University of Edinburgh and is an elected Fellow of the American Statistical Association. He was professor of Statistical Genetics at Aarhus University where, at present, he is professor emeritus.
650 0 $a Genetics $x Statistical methods. $3 186254
650 0 $a R (Computer program language) $x Statistical methods. $3 544756
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Data Analysis and Big Data. $3 913147
650 2 4 $a Biostatistics. $3 339693
650 2 4 $a Genetics. $3 189015
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Statistics for biology and health. $3 558271
856 4 0 $u https://doi.org/10.1007/978-3-031-35851-7
950 $a Mathematics and Statistics (SpringerNature-11649)