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001481061 001__ 1481061
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001481061 019__ $$a1398568578$$a1402833087
001481061 020__ $$a9783031358517
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001481061 0247_ $$a10.1007/978-3-031-35851-7$$2doi
001481061 035__ $$aSP(OCoLC)1399170835
001481061 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dEBLCP$$dSFB
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001481061 050_4 $$aQH438.4.S73
001481061 08204 $$a576.501/5195$$223/eng/20230927
001481061 1001_ $$aSorensen, Daniel.
001481061 24510 $$aStatistical learning in genetics :$$ban introduction using R /$$cDaniel Sorensen.
001481061 260__ $$aCham :$$bSpringer International Publishing AG,$$c2023.
001481061 300__ $$a1 online resource (xvi, 693 pages).
001481061 336__ $$atext$$btxt$$2rdacontent
001481061 337__ $$acomputer$$bc$$2rdamedia
001481061 338__ $$aonline resource$$bcr$$2rdacarrier
001481061 4901_ $$aStatistics for Biology and Health Series
001481061 500__ $$aDescription based upon print version of record.
001481061 506__ $$aAccess limited to authorized users.
001481061 520__ $$aThis 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.
001481061 650_6 $$aGénétique$$xMéthodes statistiques.
001481061 650_6 $$aR (Langage de programmation)$$xMéthodes statistiques.
001481061 650_0 $$aGenetics$$xStatistical methods.$$xGenetic aspects$$0(DLC)sh 85000156
001481061 650_0 $$aR (Computer program language)$$xStatistical methods.$$0(DLC)sh2002004407
001481061 655_0 $$aElectronic books.
001481061 77608 $$iPrint version:$$aSorensen, Daniel$$tStatistical Learning in Genetics$$dCham : Springer International Publishing AG,c2023$$z9783031358500
001481061 830_0 $$aStatistics for biology and health.
001481061 852__ $$bebk
001481061 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-35851-7$$zOnline Access$$91397441.1
001481061 909CO $$ooai:library.usi.edu:1481061$$pGLOBAL_SET
001481061 980__ $$aBIB
001481061 980__ $$aEBOOK
001481061 982__ $$aEbook
001481061 983__ $$aOnline
001481061 994__ $$a92$$bISE