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Generationen-Management: Konzepte, Instrumente, Good-Practice-Ansätze
by Martin KlaffkeDer demografische Wandel führt nicht nur zu einem Rückgang der Erwerbsbevölkerung, sondern auch zu neuen Ansprüchen und Vorstellungen der Beschäftigten im Arbeitsleben. Dessen ungeachtet setzen Arbeitgeber ihre Prioritäten beim Demografie-Management noch überwiegend in den Bereichen Gesundheitsförderung und Nachwuchsgewinnung. Dies reicht jedoch nicht aus, um in digitalen Zeiten eine wettbewerbsfähige Belegschaft aufzubauen. Denn für die Zukunftsfähigkeit von Unternehmen kommt es neben der Arbeitsfähigkeit vor allem auf die Kooperationsbereitschaft und das Engagement der Mitarbeitenden jeden Alters an.Generationen-Management steigert die Arbeitgeberattraktivität und nutzt das Potenzial altersgemischter Belegschaften nachhaltig zum Vorteil von Beschäftigten und Unternehmen. Hierbei sind drei Handlungsfelder wesentlich: die organisationalen und arbeitspolitischen Rahmenbedingungen gestalten, die lebenslange Leistungsfähigkeit fördern und die intergenerative Wertschätzung und Zusammenarbeit stärken. Mit umfangreichen Praxis-Tipps zeigen Expertinnen und Experten auf, wie Arbeitgeber die Fähigkeit zum Management einer Mehr-Generationen-Belegschaft gezielt entwickeln und sich einen handfesten Wettbewerbsvorteil erarbeiten können
Generations: The Real Differences Between Gen Z, Millennials, Gen X, Boomers, and Silents—and What They Mean for America's Future
by Jean M. TwengeA groundbreaking, &“lavishly informative&” (The New York Times) portrait of the six generations that currently live in the United States and how they connect, conflict, and compete with one another—from the acclaimed author of Generation Me and iGen.Upending the conventional theory that generational differences are caused by major events, Dr. Jean Twenge analyzes data on 39 million people from robust national surveys—some going back nearly a century—to show that changes in technology are the underlying driver of each generation&’s unique makeup. In this revelatory work, Twenge outlines key shifts in attitudes and lifestyle choices that define each generation regarding gender, income, politics, race, sexuality, marriage, mental health, and much more. Surprising, engaging, and informative, Generations &“gets you thinking about how appreciating generational differences can, ironically, bring us together&” (Angela Duckworth, New York Times bestselling author). It will forever change the way you view your parents, peers, coworkers, and children, no matter which generation you call your own.
Generative Artificial Intelligence: Exploring the Power and Potential of Generative AI
by Shivam R Solanki Drupad K KhublaniThis book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models. The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today’s AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation. By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI. What You Will Learn What is Generative Artificial Intelligence? What are text-to-image synthesis techniques and conditional image generation? What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques? What are text-to-video models and how do you tune them? What are large language models, and how do you tune them? Who This Book Is For Those with intermediate to advanced technical knowledge in artificial intelligence and machine learning
Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity #21)
by Joshua M. EpsteinAgent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation.This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.
Generators of Markov Chains: From a Walk in the Interior to a Dance on the Boundary (Cambridge Studies in Advanced Mathematics #190)
by Adam BobrowskiElementary treatments of Markov chains, especially those devoted to discrete-time and finite state-space theory, leave the impression that everything is smooth and easy to understand. This exposition of the works of Kolmogorov, Feller, Chung, Kato and other mathematical luminaries focuses on time-continuous chains but is not so far from being elementary itself. It reminds us once again that the first impression is false: an infinite, but denumerable state-space is where the fun begins. If you have not heard of Blackwell's example (in which all states are instantaneous), do not understand what the minimal process is, or do not know what happens after explosion, dive right in. But beware lest you are enchanted: 'There are more spells than your commonplace magicians ever dreamed of.'
Generic Coarse Geometry of Leaves (Lecture Notes in Mathematics #2223)
by Jesús A. Álvarez López Alberto CandelThis book provides a detailed introduction to the coarse quasi-isometry of leaves of a foliated space and describes the cases where the generic leaves have the same quasi-isometric invariants. Every leaf of a compact foliated space has an induced coarse quasi-isometry type, represented by the coarse metric defined by the length of plaque chains given by any finite foliated atlas. When there are dense leaves either all dense leaves without holonomy are uniformly coarsely quasi-isometric to each other, or else every leaf is coarsely quasi-isometric to just meagerly many other leaves. Moreover, if all leaves are dense, the first alternative is characterized by a condition on the leaves called coarse quasi-symmetry. Similar results are proved for more specific coarse invariants, like growth type, asymptotic dimension, and amenability. The Higson corona of the leaves is also studied. All the results are richly illustrated with examples. The book is primarily aimed at researchers on foliated spaces. More generally, specialists in geometric analysis, topological dynamics, or metric geometry may also benefit from it.
Generic Data Structures and Algorithms in Go: An Applied Approach Using Concurrency, Genericity and Heuristics
by Richard WienerAdvance your understanding of generic data structures and algorithms and their applications using Go and the effective use of concurrency. You are invited on a journey that aims to improve your programming and problem-solving skills. This book takes you to the next step by showing how to get your programs to work efficiently as well as correctly. As you explore many data structures and the algorithms and applications associated with them, you'll focus on the trade-offs between speed and storage and the benefits of deploying concurrency when appropriate. This book will demonstrate the huge increases in application performance that are possible. The presentation of classic data structures and techniques of algorithm design (greedy, divide and conquer, branch-and-bound to name a few) provides an essential foundation and toolkit for problem solving. But this book goes further by presenting heuristic algorithms and their implementations for solving computationally intractable combinatoric optimization problems such as the travelling salesperson problem. Simulated annealing and genetic algorithms are among the techniques used.The consistent style of coding used throughout this book exploits Go’s ability to implement abstract, generic and constrained generic data types without the use of classes. Although some familiarity with Go is assumed, this book should advance your ability to use Go to tackle server-side applications, games, machine learning, information retrieval and other application domains where speed and storage efficiency is essential.What You'll LearnExplore classical data structures and algorithms aimed at making your applications run faster or require less storageUse the new generic features of Go to build reusable data structuresUtilize concurrency for maximizing application performanceSee the power of heuristic algorithms for computationally intractable problemsEnhance and improve your Go programming skillsWho This Book Is ForPracticing Go software developers and students who wish to advance their programming and problem-solving skills and experience the excitement and see the benefits of using generic data structures and algorithms that utilize concurrency whenever possible.
Genericity in Nonlinear Analysis
by Simeon Reich Alexander J. ZaslavskiThis book presents an extensive collection of state-of-the-art results and references in nonlinear functional analysis demonstrating how the generic approach proves to be very useful in solving many interesting and important problems. Nonlinear analysis plays an ever-increasing role in theoretical and applied mathematics, as well as in many other areas of science such as engineering, statistics, computer science, economics, finance, and medicine. The text may be used as supplementary material for graduate courses in nonlinear functional analysis, optimization theory and approximation theory, and is a treasure trove for instructors, researchers, and practitioners in mathematics and in the mathematical sciences. Each chapter is self-contained; proofs are solid and carefully communicated. Genericity in Nonlinear Analysis is the first book to systematically present the generic approach to nonlinear analysis. Topics presented include convergence analysis of powers and infinite products via the Baire Category Theorem, fixed point theory of both single- and set-valued mappings, best approximation problems, discrete and continuous descent methods for minimization in a general Banach space, and the structure of minimal energy configurations with rational numbers in the Aubry-Mather theory.
Genes, Environments and Interactions: Evolutionary and Quantitative Genetics Brought Up-to-date
by José M Álvarez-CastroGenetic effects are the core concepts from which quantitative genetics and the evolutionary synthesis emerged. The groundbreaking theory of genetic effects was first proposed over a century ago. This book revises that theory, both conceptually and mathematically, and brings it up-to-date. The theory here compiled is supplemented with non-previously-published developments covering the broadest spectrum of simultaneously multiallelic and multilocus architectures with autosomal and sex-linked lociArbitrary interactions (dominance, gene-gene, gene-environment, gene-sex, and parent-of-origin interactions) are accounted forBoth effects of allele substitutions from the reference of individual genotypes and in the context of populations are worked outPopulations are considered regardless of any departures from equilibrium frequencies (including both departures from Hardy-Weinberg, departures from linkage equilibrium, and non-random associations between/among genes and environments)All developments are derived under the same mathematical framework, so that transformations of genetic effects between different contexts are easily allowed In brief, this book enables novel applications to current empirical paradigms (like gene-mapping and genomic prediction) while adhering to the classical conceptualization of genetic effects and variance decomposition that let quantitative genetics and the evolutionary synthesis flourish. All relevant concepts are carefully clarified and discussed from a historical perspective. The theoretical developments presented in the book are illustrated by built-in cases and applications with real data. Reassuringly, the adequacy of the theory here presented is corroborated based on the fundamentals of model development.
Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions
by Frances BuontempoSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. <P><P> Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.
Genetic Algorithms and Machine Learning for Programmers
by Frances BuontempoSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions.Build genetic algorithms.Make nature-inspired swarms with ants, bees and particles.Create Monte Carlo simulations.Investigate cellular automata.Find minima and maxima, using hill climbing and simulated annealing.Try selection methods, including tournament and roulette wheels.Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
Genetic Algorithms for Pattern Recognition (CRC Press Revivals)
by Sankar K. Pal Paul P. WangSolving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.
Genetic and Evolutionary Computing: Proceedings of the Fifteenth International Conference on Genetic and Evolutionary Computing (Volume I), October 6–8, 2023, Kaohsiung, Taiwan (Lecture Notes in Electrical Engineering #1145)
by Jerry Chun-Wei Lin Chin-Shiuh Shieh Mong-Fong Horng Shu-Chuan ChuThis first book of conference proceedings contains selected papers presented at ICGEC 2023, the 15th International Conference on Genetic and Evolutionary Computing, held on October 6–8, 2023, in Kaohsiung, Taiwan. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers know the up-to-date techniques of the mentioned topics, including swarm intelligence and its applications, operational technologies and networked multimedia applications, wearable computing and intelligent data hiding, image processing and intelligent applications, and intelligent multimedia tools and applications. It helps readers bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
Genetic and Evolutionary Computing: Proceedings of the Fifteenth International Conference on Genetic and Evolutionary Computing (Volume II), October 6-8, 2023, Kaohsiung, Taiwan (Lecture Notes in Electrical Engineering #1114)
by Jeng-Shyang Pan Zhigeng Pan Pei Hu Jerry Chun-Wei LinThis second volume of conference proceedings contains selected papers presented at ICGEC 2023, the 15th International Conference on Genetic and Evolutionary Computing, held on October 6-8, 2023 in Kaohsiung, Taiwan. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers may know the up-to-date techniques of the mentioned topics, including technologies for next-generation network environments, recent progress in computational electromagnetic dynamics, future cyber security, privacy and forensics for advanced systems, data mining techniques and its applications, optimization models in deep learning and machine learning. It will help readers bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
Genetic Programming: 27th European Conference, EuroGP 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings (Lecture Notes in Computer Science #14631)
by Mario Giacobini Bing Xue Luca ManzoniThis book constitutes the refereed proceedings of the 27th European Conference on Genetic Programming, EuroGP 2024, held in Aberystwyth, UK, April 3–5, 2024 and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EvoApplications.The 13 papers (9 selected for long presentation and 4 for short presentation) collected in this book were carefully reviewed and selected from 24 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover topics including developing new variants of GP algorithms, as well as exploring GP applications to the optimization of machine learning methods and the evolution of control policies.
Genetic Theory for Cubic Graphs
by Pouya Baniasadi Vladimir Ejov Jerzy A. Filar Michael HaythorpeThis book was motivated by the notion that some of the underlying difficulty in challenging instances of graph-based problems (e. g. , the Traveling Salesman Problem) may be "inherited" from simpler graphs which - in an appropriate sense - could be seen as "ancestors" of the given graph instance. The authors propose a partitioning of the set of unlabeled, connected cubic graphs into two disjoint subsets named genes and descendants, where the cardinality of the descendants dominates that of the genes. The key distinction between the two subsets is the presence of special edge cut sets, called cubic crackers, in the descendants. The book begins by proving that any given descendant may be constructed by starting from a finite set of genes and introducing the required cubic crackers through the use of six special operations, called breeding operations. It shows that each breeding operation is invertible, and these inverse operations are examined. It is therefore possible, for any given descendant, to identify a family of genes that could be used to generate the descendant. The authors refer to such a family of genes as a "complete family of ancestor genes" for that particular descendant. The book proves the fundamental, although quite unexpected, result that any given descendant has exactly one complete family of ancestor genes. This result indicates that the particular combination of breeding operations used strikes the right balance between ensuring that every descendant may be constructed while permitting only one generating set. The result that any descendant can be constructed from a unique set of ancestor genes indicates that most of the structure in the descendant has been, in some way, inherited from that, very special, complete family of ancestor genes, with the remaining structure induced by the breeding operations. After establishing this, the authors proceed to investigate a number of graph theoretic properties: Hamiltonicity, bipartiteness, and planarity, and prove results linking properties of the descendant to those of the ancestor genes. They develop necessary (and in some cases, sufficient) conditions for a descendant to contain a property in terms of the properties of its ancestor genes. These results motivate the development of parallelizable heuristics that first decompose a graph into ancestor genes, and then consider the genes individually. In particular, they provide such a heuristic for the Hamiltonian cycle problem. Additionally, a framework for constructing graphs with desired properties is developed, which shows how many (known) graphs that constitute counterexamples of conjectures could be easily found.
Genetically Modified Organisms, Grade 7: STEM Road Map for Middle School (STEM Road Map Curriculum Series)
by Carla C. Johnson Janet B. Walton Erin E. Peters-BurtonWhat if you could challenge your seventh graders to become informed citizens by analyzing real-world implications of GMOs? With this volume in the STEM Road Map Curriculum Series, you can! Genetically Modified Organisms outlines a journey that will steer your students toward authentic problem solving while grounding them in integrated STEM disciplines. Like the other volumes in the series, this book is designed to meet the growing need to infuse real-world learning into K–12 classrooms. This interdisciplinary, five-lesson module uses project- and problem-based learning to help students investigate the opportunities and challenges of GMO production and consumption. Working in teams, students will create a documentary communicating the health, social, and economic aspects of GMO production and consumption. To support this goal, students will do the following: • Use the Internet and other sources to build knowledge of an issue, and recognize and value stakeholders and their viewpoints in an issue. • Explore the relationship among local, state, and federal legislation related to GMOs. • Understand the role of cost-benefit analysis in making informed economic decisions. • Develop skills to evaluate arguments, create and communicate individual understanding and perspectives. • Gain a deeper understanding that structure and function are related by examining plants and how the environment and genetics influences structure. • Gain a better understanding of what tools humans have developed to genetically alter organisms for human benefit. The STEM Road Map Curriculum Series is anchored in the Next Generation Science Standards, the Common Core State Standards, and the Framework for 21st Century Learning. In-depth and flexible, Genetically Modified Organisms can be used as a whole unit or in part to meet the needs of districts, schools, and teachers who are charting a course toward an integrated STEM approach.
Geniale Frauen in der Wissenschaft: Versteckte Beiträge, die die Welt verändert haben
by Lars JaegerObwohl Frauen schon früh das wissenschaftliche Denken mitgeprägt haben, sichtbar geworden sind sie fast nie. Dieses Ungleichgewicht setzt sich bis heute fort, auch wenn es aktuell weit mehr Wissenschaftlerinnen gibt als jemals zuvor. Lars Jaeger spannt einen Bogen von der Antike bis heute und porträtiert in essayartigen Einführungen das Leben und Wirken der wohl bedeutendsten weiblichen Naturwissenschaftlerinnen und Mathematikerinnen. Von Hypatia von Alexandria über Émilie du Châtelet und Emmy Noether bis hin zu Lisa Randall, sie alle haben Großes geleistet, die Wissenschaft entscheidend vorangebracht und konnten dennoch oft nicht aus dem Schatten ihrer männlichen Kollegen treten.Neben den spannenden Porträts der einzelnen Wissenschaftlerinnen sowie einer detaillierten und anschaulichen Darstellung ihrer wissenschaftlichen Leistungen beleuchtet dieses Sachbuch auch das Geschlechterverhältnis in der Wissenschaft, das sich nur quälend langsam zugunsten eines fairen Verhältnisses für die Frauen entwickelt.
Genocide: State Power and Mass Murder (Issues In Contemporary Civilization Ser.)
by James BaldwinThis book is dedicated to a consideration of genocide in the context of political sociology. It demonstrates that the underlining predicates of sociology give scant consideration to basic issues of life and death in favor of distinctly derivative issues of social structure and social function.
Genome Data Analysis (Learning Materials in Biosciences)
by Ju Han KimThis textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
Genome-Scale Algorithm Design: Biological Sequence Analysis in the Era of High-Throughput Sequencing
by Veli Mäkinen Djamal Belazzougui Fabio Cunial Alexandru I. TomescuHigh-throughput sequencing has revolutionised the field of biological sequence analysis. Its application has enabled researchers to address important biological questions, often for the first time. This book provides an integrated presentation of the fundamental algorithms and data structures that power modern sequence analysis workflows. The topics covered range from the foundations of biological sequence analysis (alignments and hidden Markov models), to classical index structures (k-mer indexes, suffix arrays and suffix trees), Burrows-Wheeler indexes, graph algorithms and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualisations, exercises and problems, each chosen to reflect the steps of large-scale sequencing projects, including read alignment, variant calling, haplotyping, fragment assembly, alignment-free genome comparison, transcript prediction and analysis of metagenomic samples. Each biological problem is accompanied by precise formulations, providing graduate students and researchers in bioinformatics and computer science with a powerful toolkit for the emerging applications of high-throughput sequencing.
Genomics and Bioinformatics
by Tore SamuelssonWith the arrival of genomics and genome sequencing projects, biology has been transformed into an incredibly data-rich science. The vast amount of information generated has made computational analysis critical and has increased demand for skilled bioinformaticians. Designed for biologists without previous programming experience, this textbook provides a hands-on introduction to Unix, Perl and other tools used in sequence bioinformatics. Relevant biological topics are used throughout the book and are combined with practical bioinformatics examples, leading students through the process from biological problem to computational solution. All of the Perl scripts, sequence and database files used in the book are available for download at the accompanying website, allowing the reader to easily follow each example using their own computer. Programming examples are kept at an introductory level, avoiding complex mathematics that students often find daunting. The book demonstrates that even simple programs can provide powerful solutions to many complex bioinformatics problems.
Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods
by David R. BickelStatisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research.Key Features:* dice games and exercises, including one using interactive software, for teaching the concepts in the classroom* examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data* gradual introduction to the mathematical equations needed* how to choose between different methods of multiple hypothesis testing* how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates* guidance through the minefield of current criticisms of p values* material on non-Bayesian prior p values and posterior p values not previously published
Genomics Data Analysis for Crop Improvement (Springer Protocols Handbooks)
by Priyanka Anjoy Kuldeep Kumar Girish Chandra Kishor GaikwadThis book addresses complex problems associated with crop improvement programs, using a wide range of programming solutions, for genomics data handling and sustainable agriculture. It describes important concepts in genomics data analysis and sequence-based mapping approaches along with references. The book contains 16 chapters on recent developments in several methods of genomic data analysis for crop improvements and sustainable agriculture, all authored by eminent researchers who are experts in their fields. These chapters focus on applications of a wide range of key bioinformatics topics, including assembly, annotation, and visualization of next-generation sequencing (NGS) data; expression profiles of coding and noncoding RNA; statistical and quantitative genetics; trait-based association analysis, quantitative trait loci (QTL) mapping, and artificial intelligence in genomic studies. Real examples and case studies in the book will come in handy when applying the techniques. The relative scarcity of reference materials covering bioinformatics applications as compared with the readily available books also enhances the utility of this book. The targeted readers of the book are scientists, researchers, and bioinformaticians from genomics and advanced breeding in different areas. The book will appeal to the applied researchers engaged in crop improvements and sustainable agriculture by using bioinformatics tools, students, research project leaders, and practitioners from the various marginal disciplines and interdisciplinary research.
Genomics in the Cloud: Using Docker, GATK, and WDL in Terra
by Brian O'Connor Geraldine A. Van der AuweraData in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytes—or over 50 million gigabytes—of genomic data, and they’re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud?With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian O’Connor of the UC Santa Cruz Genomics Institute, guide you through the process. You’ll learn by working with real data and genomics algorithms from the field. <p><p>This book covers: <li>Essential genomics and computing technology background <li>Basic cloud computing operations <li>Getting started with GATK, plus three major GATK Best Practices pipelines <li>Automating analysis with scripted workflows using WDL and Cromwell <li>Scaling up workflow execution in the cloud, including parallelization and cost optimization <li>Interactive analysis in the cloud using Jupyter notebooks <li>Secure collaboration and computational reproducibility using Terra