04823nam 22006255 450 991101165600332120250625125948.0978303186274810.1007/978-3-031-86274-8(CKB)39450095700041(MiAaPQ)EBC32176050(Au-PeEL)EBL32176050(OCoLC)1525619490(DE-He213)978-3-031-86274-8(EXLCZ)993945009570004120250625d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierStatistical Learning in Genetics An Introduction Using R /by Daniel Sorensen2nd ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (1051 pages)Statistics for Biology and Health,2197-56719783031862731 - 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.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 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.Statistics for Biology and Health,2197-5671StatisticsGeneticsQuantitative researchBiometryStatistical Theory and MethodsGeneticsData Analysis and Big DataBiostatisticsStatistics.Genetics.Quantitative research.Biometry.Statistical Theory and Methods.Genetics.Data Analysis and Big Data.Biostatistics.576.5015195Sorensen Daniel1429691MiAaPQMiAaPQMiAaPQBOOK9911011656003321Statistical Learning in Genetics3568944UNINA03707nam 22006375 450 991104765660332120251124120536.03-032-07735-410.1007/978-3-032-07735-6(MiAaPQ)EBC32428375(Au-PeEL)EBL32428375(CKB)43713275000041(DE-He213)978-3-032-07735-6(EXLCZ)994371327500004120251124d2026 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierData Science and Network Engineering Proceedings of ICDSNE 2025 /edited by Suyel Namasudra, Nirmalya Kar, Sarat Kumar Patra, Byung-Gyu Kim1st ed. 2026.Cham :Springer Nature Switzerland :Imprint: Springer,2026.1 online resource (524 pages)Lecture Notes in Networks and Systems,2367-3389 ;16683-032-07734-6 Ensemble Classifier for Real-Time Breast Cancer Classification on Histopathology Images -- A Smart Surveillance Framework for Real-Time Suspicious Activity Detection and Automated Alert Generation Using YOLOv8 -- Enhancing E-Commerce Trust: An Integrated Product Recommendation and Fake Review Detection System -- Hardware-Efficient Neural Network for Voice Disorder Classification from Multi-Source Datasets -- Predictive Maintenance on C-MAPSS Using LSTM Variants and Attention -- Unveiling Ebola-Human Protein Links through Network Embedding and Unsupervised Machine Learning -- Discount Optimisation in Food Delivery Using Machine Learning.This book includes research papers presented at the International Conference on Data Science and Network Engineering (ICDSNE 2025) organized by the Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India, during July 18–19, 2025. It includes research work from researchers, academicians, business executives, and industry professionals for solving real-life problems by using the advancements and applications of data science and network engineering. This book covers many advanced topics, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), computer networks, blockchain, security and privacy, Internet of things (IoT), cloud computing, big data, supply chain management, and many more. Different sections of this book are highly beneficial for the researchers, who are working in the field of data science and network engineering.Lecture Notes in Networks and Systems,2367-3389 ;1668Artificial intelligenceTelecommunicationData structures (Computer science)Information theoryCooperating objects (Computer systems)Artificial IntelligenceCommunications Engineering, NetworksData Structures and Information TheoryCyber-Physical SystemsArtificial intelligence.Telecommunication.Data structures (Computer science)Information theory.Cooperating objects (Computer systems)Artificial Intelligence.Communications Engineering, Networks.Data Structures and Information Theory.Cyber-Physical Systems.006.3Namasudra Suyel1437818MiAaPQMiAaPQMiAaPQBOOK9911047656603321Data Science and Network Engineering4472025UNINA