LEADER 04823nam 22006255 450 001 9911011656003321 005 20250625125948.0 010 $a9783031862748 024 7 $a10.1007/978-3-031-86274-8 035 $a(CKB)39450095700041 035 $a(MiAaPQ)EBC32176050 035 $a(Au-PeEL)EBL32176050 035 $a(OCoLC)1525619490 035 $a(DE-He213)978-3-031-86274-8 035 $a(EXLCZ)9939450095700041 100 $a20250625d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Learning in Genetics $eAn Introduction Using R /$fby Daniel Sorensen 205 $a2nd ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (1051 pages) 225 1 $aStatistics for Biology and Health,$x2197-5671 311 08$a9783031862731 327 $a- 1. Overview -- Part I: Fitting Likelihood and Bayesian Models -- 2. Likelihood -- 3. Computing the Likelihood -- 4. Bayesian Methods -- 5. McMC in Practice -- Part II: Prediction -- 6. Fundamentals of Prediction -- 7. Shrinkage Methods -- 8. Digression on Multiple Testing: False Discovery Rates -- 9. Binary Data -- 10. Bayesian Prediction and Model Checking -- 11. Nonparametric Methods: A Selected Overview -- Part III: Exercises and Solutions -- 12. Exercises -- 13. Solution to Exercises. 330 $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 may lack the formal mathematical background of the professional statistician. For this reason, considerably more detailed explanations and derivations are offered. Examples are used profusely and a large proportion involves programming with the open-source package R. The code needed to solve the exercises is provided and it can be downloaded, allowing students to experiment by running the programs on their own computer. Part I presents methods of inference and computation that are appropriate for 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 the 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. This second edition has benefited from many clarifications and extensions of themes discussed in the first edition. 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. 410 0$aStatistics for Biology and Health,$x2197-5671 606 $aStatistics 606 $aGenetics 606 $aQuantitative research 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aGenetics 606 $aData Analysis and Big Data 606 $aBiostatistics 615 0$aStatistics. 615 0$aGenetics. 615 0$aQuantitative research. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aGenetics. 615 24$aData Analysis and Big Data. 615 24$aBiostatistics. 676 $a576.5015195 700 $aSorensen$b Daniel$01429691 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911011656003321 996 $aStatistical Learning in Genetics$93568944 997 $aUNINA