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| differential gene expression analysis: Gene Expression Analysis Nalini Raghavachari, Natàlia Garcia-Reyero, 2018-05-17 This volume provides experimental and bioinformatics approaches related to different aspects of gene expression analysis. Divided in three sections chapters detail wet-lab protocols, bioinformatics approaches, single-cell gene expression, highly multiplexed amplicon sequencing, multi-omics techniques, and targeted sequencing. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Gene Expression Analysis: Methods and Protocols aims provide useful information to researchers worldwide. |
| differential gene expression analysis: Bioinformatics and Computational Biology Solutions Using R and Bioconductor Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit, 2005-12-29 Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers. |
| differential gene expression analysis: Computational Genomics with R Altuna Akalin, 2020-12-16 Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015. |
| differential gene expression analysis: Gene Expression Data Analysis Pankaj Barah, Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita, 2021-11-08 Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences |
| differential gene expression analysis: RNA-seq Data Analysis Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong, 2014-09-19 The State of the Art in Transcriptome AnalysisRNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript le |
| differential gene expression analysis: Bayesian Inference for Gene Expression and Proteomics Kim-Anh Do, Peter Müller, Marina Vannucci, 2006-07-24 Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation. |
| differential gene expression analysis: The Analysis of Gene Expression Data Giovanni Parmigiani, Elizabeth S. Garett, Rafael A. Irizarry, Scott L. Zeger, 2006-04-11 This book presents practical approaches for the analysis of data from gene expression micro-arrays. It describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools. The materials presented cover a range of software tools designed for varied audiences. |
| differential gene expression analysis: Machine Learning Paradigms George A. Tsihrintzis, Maria Virvou, Evangelos Sakkopoulos, Lakhmi C. Jain, 2019-07-06 This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most. |
| differential gene expression analysis: Statistical Analysis of Next Generation Sequencing Data Somnath Datta, Dan Nettleton, 2016-09-17 Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics. |
| differential gene expression analysis: Mixed-Effects Models in S and S-PLUS José C. Pinheiro, Douglas Bates, 2009-04-15 R, linear models, random, fixed, data, analysis, fit. |
| differential gene expression analysis: Molecular Pathology in Cancer Research Sunil R. Lakhani, Stephen B. Fox, 2017-01-20 The aim of the book is to discuss the application of molecular pathology in cancer research, and its contribution in the classification of different tumors and identification of potential molecular targets, as well as how this knowledge may be translated into clinical practice, and the huge impact this field is likely to have in the next 5 to 10 years. |
| differential gene expression analysis: Genomics Data Analysis David R. Bickel, 2019-09-24 Statisticians 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 |
| differential gene expression analysis: Advanced Analysis of Gene Expression Microarray Data Aidong Zhang, 2006 Focuses on the development and application of the latest advanced data mining, machine learning, and visualization techniques for the identification of interesting, significant, and novel patterns in gene expression microarray data. Describes cutting-edge methods for analyzing gene expression microarray data. Coverage includes gene-based analysis, sample-based analysis, pattern-based analysis and visualization tools. |
| differential gene expression analysis: Fusarium wilt Jeffrey Coleman, 2021-10-23 This volume provides a collection of molecular protocols detailing the most common and modern techniques on fusarium wilt. Chapters guide readers through methods on initial isolation, molecular-based identification, genome characterization, generation of mutants, and characterization of interactions with other organisms including host plants. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Fusarium wilt: Methods and Protocols aims to be a valuable resource for mycologists, plant pathologists, microbiologists, geneticists, and other scientists that have an interest in members of the Fusarium oxysporum species complex or closely related fungi. |
| differential gene expression analysis: Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman, Jennifer Hill, 2007 This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. |
| differential gene expression analysis: Biologically Inspired Techniques in Many-Criteria Decision Making Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick, Sung-Bae Cho, Margarita N. Favorskaya, 2020-01-21 This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems. This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing. |
| differential gene expression analysis: Algorithms for Minimization Without Derivatives Richard P. Brent, 2013-06-10 DIVOutstanding text for graduate students and research workers proposes improvements to existing algorithms, extends their related mathematical theories, and offers details on new algorithms for approximating local and global minima. /div |
| differential gene expression analysis: Plant Germline Development Anja Schmidt, 2017-09-22 This detailed volume explores common and numerous specialized methods to study various aspects of plant germline development and targeted manipulation, including imaging and hybridization techniques to study cell-type specification, cell lineage, signaling and hormones, cell cycle, and the cytoskeleton. In addition, cell-type specific methods for targeted ablation or isolation are provided, protocols to apply “omics” technologies and to perform bioinformatics data analysis, as well as methods relevant for aspects of biotechnology or plant breeding. This includes protocols that are relevant for the targeted manipulation of pathways, for crop plant transformation, or for conditional induction of phenotypes. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Plant Germline Development: Methods and Protocols serves as a comprehensive guide not only to studying basic questions related to different aspects of plant reproductive development but also for state of the art methods, in addition to being a source of inspiration for new approaches and research questions in many laboratories. |
| differential gene expression analysis: Statistical Analysis of Gene Expression Microarray Data Terry Speed, 2003-03-26 Although less than a decade old, the field of microarray data analysis is now thriving and growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies |
| differential gene expression analysis: Molecular-Genetic and Statistical Techniques for Behavioral and Neural Research Robert T. Gerlai, 2018-04-24 Molecular-Genetic and Statistical Techniques for Behavioral and Neural Research presents the most exciting molecular and recombinant DNA techniques used in the analysis of brain function and behavior, a critical piece of the puzzle for clinicians, scientists, course instructors and advanced undergraduate and graduate students. Chapters examine neuroinformatics, genetic and neurobehavioral databases and data mining, also providing an analysis of natural genetic variation and principles and applications of forward (mutagenesis) and reverse genetics (gene targeting). In addition, the book discusses gene expression and its role in brain function and behavior, along with ethical issues in the use of animals in genetics testing. Written and edited by leading international experts, this book provides a clear presentation of the frontiers of basic research as well as translationally relevant techniques that are used by neurobehavioral geneticists. - Focuses on new techniques, including electrocorticography, functional mapping, stereo EEG, motor evoked potentials, optical coherence tomography, magnetoencephalography, laser evoked potentials, transmagnetic stimulation, and motor evoked potentials - Presents the most exciting molecular and recombinant DNA techniques used in the analysis of brain function and behavior - Written and edited by leading international experts |
| differential gene expression analysis: Statistical Genomics Ewy Mathé, Sean Davis, 2016-03-24 This volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad-hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data. |
| differential gene expression analysis: Single Cell Methods Valentina Proserpio, 2019 This volume provides a comprehensive overview for investigating biology at the level of individual cells. Chapters are organized into eight parts detailing a single-cell lab, single cell DNA-seq, RNA-seq, single cell proteomic and epigenetic, single cell multi-omics, single cell screening, and single cell live imaging. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Single Cell Methods: Sequencing and Proteomics aims to make each experiment easily reproducible in every lab. |
| differential gene expression analysis: Computational Epigenetics and Diseases , 2019-02-06 Computational Epigenetics and Diseases, written by leading scientists in this evolving field, provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases. In particular, the major computational tools, databases, and strategies for computational epigenetics analysis, for example, DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA, are summarized, in the context of human diseases. This book discusses bioinformatics methods for epigenetic analysis specifically applied to human conditions such as aging, atherosclerosis, diabetes mellitus, schizophrenia, bipolar disorder, Alzheimer disease, Parkinson disease, liver and autoimmune disorders, and reproductive and respiratory diseases. Additionally, different organ cancers, such as breast, lung, and colon, are discussed. This book is a valuable source for graduate students and researchers in genetics and bioinformatics, and several biomedical field members interested in applying computational epigenetics in their research. - Provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases - Summarizes the major computational tools, databases, and strategies for computational epigenetics analysis, such as DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA - Covers the major milestones and future directions of computational epigenetics in various kinds of human diseases such as aging, atherosclerosis, diabetes, heart disease, neurological disorders, cancers, blood disorders, liver diseases, reproductive diseases, respiratory diseases, autoimmune diseases, human imprinting disorders, and infectious diseases |
| differential gene expression analysis: Computational Systems Biology Tao Huang, 2018-03-14 This volume introduces the reader to the latest experimental and bioinformatics methods for DNA sequencing, RNA sequencing, cell-free tumour DNA sequencing, single cell sequencing, single-cell proteomics and metabolomics. Chapters detail advanced analysis methods, such as Genome-Wide Association Studies (GWAS), machine learning, reconstruction and analysis of gene regulatory networks and differential coexpression network analysis, and gave a practical guide for how to choose and use the right algorithm or software to handle specific high throughput data or multi-omics data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Systems Biology: Methods and Protocols aims to ensure successful results in the further study of this vital field. |
| differential gene expression analysis: Molecular Biology of the Cell , 2002 |
| differential gene expression analysis: Optimal Bayesian Classification Lori A. Dalton, Edward R. Dougherty, 2019 The most basic problem of engineering is the design of optimal operators. Design takes different forms depending on the random process constituting the scientific model and the operator class of interest. This book treats classification, where the underlying random process is a feature-label distribution, and an optimal operator is a Bayes classifier, which is a classifier minimizing the classification error. With sufficient knowledge we can construct the feature-label distribution and thereby find a Bayes classifier. Rarely, do we possess such knowledge. On the other hand, if we had unlimited data, we could accurately estimate the feature-label distribution and obtain a Bayes classifier. Rarely do we possess sufficient data. The aim of this book is to best use whatever knowledge and data are available to design a classifier. The book takes a Bayesian approach to modeling the feature-label distribution and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions. In this way it takes full advantage of knowledge regarding the underlying system and the available data. Its origins lie in the need to estimate classifier error when there is insufficient data to hold out test data, in which case an optimal error estimate can be obtained relative to the uncertainty class. A natural next step is to forgo classical ad hoc classifier design and simply find an optimal classifier relative to the posterior distribution over the uncertainty class-this being an optimal Bayesian classifier-- |
| differential gene expression analysis: Hidden Markov Models David R. Westhead, M. S. Vijayabaskar, 2017-02-22 This volume aims to provide a new perspective on the broader usage of Hidden Markov Models (HMMs) in biology. Hidden Markov Models: Methods and Protocols guides readers through chapters on biological systems; ranging from single biomolecule, cellular level, and to organism level and the use of HMMs in unravelling the complex mechanisms that govern these complex systems. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Hidden Markov Models: Methods and Protocols aims to demonstrate the impact of HMM in biology and inspire new research. |
| differential gene expression analysis: Applications of RNA-Seq in Biology and Medicine Irina Vlasova-St. Louis, 2021-10-13 This book evaluates and comprehensively summarizes the scientific findings that have been achieved through RNA-sequencing (RNA-Seq) technology. RNA-Seq transcriptome profiling of healthy and diseased tissues allows FOR understanding the alterations in cellular phenotypes through the expression of differentially spliced RNA isoforms. Assessment of gene expression by RNA-Seq provides new insight into host response to pathogens, drugs, allergens, and other environmental triggers. RNA-Seq allows us to accurately capture all subtypes of RNA molecules, in any sequenced organism or single-cell type, under different experimental conditions. Merging genomics and transcriptomic profiling provides novel information underlying causative DNA mutations. Combining RNA-Seq with immunoprecipitation and cross-linking techniques is a clever multi-omics strategy assessing transcriptional, post-transcriptional and post-translational levels of gene expression regulation. |
| differential gene expression analysis: The EM Algorithm and Extensions Geoffrey J. McLachlan, Thriyambakam Krishnan, 2007-11-09 The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm. |
| differential gene expression analysis: Seldin and Giebisch's The Kidney Robert J. Alpern, Steven C. Hebert, 2007-10-10 A classic nephrology reference for over 20 years, Seldin & Giebisch's The Kidney, is the acknowledged authority on renal physiology and pathophysiology. The fourth edition follows the changed focus of nephrology research to the study of how individual molecules work together to affect cellular and organ function, emphasizing the mechanisms of disease. With over 40 new chapters and over 1000 illustrations, this edition offers the most in-depth discussion anywhere of the physiologic and pathophysiologic processes of renal disease. Comprehensive, authoritative coverage progresses from molecular biology and cell physiology to clinical issues regarding renal function and dysfunction. If you research the development of normal renal function or the mechanisms underlying renal disease, Seldin & Giebisch's The Kidney is your number one source for information.* Offers the most comprehensive coverage of fluid and electrolyte regulation and dysregulation in 51 completely revised chapters unlike Brenner & Rector's The Kidney which devotes only 7 chapters to this topic.* Includes 3 sections, 31 chapters, devoted to regulation and disorders of acid-base homeostasis, and epithelial and nonepithelial transport regulation. Brenner & Rector's only devotes 5 chapters to these topics.* Previous three editions edited by Donald Seldin and Gerhard Giebisch, world renowned names in nephrology. The title for the fourth edition has been changed to reflect their considerable work on previous editions and they have also written the forward for this edition. * Over 20 million adults over age 20 have chronic kidney disease with the number of people diagnosed doubling each decade making it America's ninth leading cause of death. |
| differential gene expression analysis: Gene Quantification Francois Ferre, 2012-12-06 Geneticists and molecular biologists have been interested in quantifying genes and their products for many years and for various reasons (Bishop, 1974). Early molecular methods were based on molecular hybridization, and were devised shortly after Marmur and Doty (1961) first showed that denaturation of the double helix could be reversed - that the process of molecular reassociation was exquisitely sequence dependent. Gillespie and Spiegelman (1965) developed a way of using the method to titrate the number of copies of a probe within a target sequence in which the target sequence was fixed to a membrane support prior to hybridization with the probe - typically a RNA. Thus, this was a precursor to many of the methods still in use, and indeed under development, today. Early examples of the application of these methods included the measurement of the copy numbers in gene families such as the ribosomal genes and the immunoglo bulin family. Amplification of genes in tumors and in response to drug treatment was discovered by this method. In the same period, methods were invented for estimating gene num bers based on the kinetics of the reassociation process - the so-called Cot analysis. This method, which exploits the dependence of the rate of reassociation on the concentration of the two strands, revealed the presence of repeated sequences in the DNA of higher eukaryotes (Britten and Kohne, 1968). An adaptation to RNA, Rot analysis (Melli and Bishop, 1969), was used to measure the abundance of RNAs in a mixed population. |
| differential gene expression analysis: Encyclopedia of Bioinformatics and Computational Biology , 2018-08-21 Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases |
| differential gene expression analysis: Interactive and Dynamic Graphics for Data Analysis Dianne Cook, Deborah F. Swayne, 2007-12-12 This book is about using interactive and dynamic plots on a computer screen as part of data exploration and modeling, both alone and as a partner with static graphics and non-graphical computational methods. The area of int- active and dynamic data visualization emerged within statistics as part of research on exploratory data analysis in the late 1960s, and it remains an active subject of research today, as its use in practice continues to grow. It now makes substantial contributions within computer science as well, as part of the growing ?elds of information visualization and data mining, especially visual data mining. The material in this book includes: • An introduction to data visualization, explaining how it di?ers from other types of visualization. • Adescriptionofourtoolboxofinteractiveanddynamicgraphicalmethods. • An approach for exploring missing values in data. • An explanation of the use of these tools in cluster analysis and supervised classi?cation. • An overview of additional material available on the web. • A description of the data used in the analyses and exercises. The book’s examples use the software R and GGobi. R (Ihaka & Gent- man 1996, RDevelopment CoreTeam2006) isafreesoftware environment for statistical computing and graphics; it is most often used from the command line, provides a wide variety of statistical methods, and includes high–quality staticgraphics.RaroseintheStatisticsDepartmentoftheUniversityofAu- land and is now developed and maintained by a global collaborative e?ort. |
| differential gene expression analysis: Modern Statistics for Modern Biology SUSAN. HUBER HOLMES (WOLFGANG.), Wolfgang Huber, 2018 |
| differential gene expression analysis: Applied Bioinformatics Paul Maria Selzer, Richard Marhöfer, Andreas Rohwer, 2008-01-18 At last, here is a baseline book for anyone who is confused by cryptic computer programs, algorithms and formulae, but wants to learn about applied bioinformatics. Now, anyone who can operate a PC, standard software and the internet can also learn to understand the biological basis of bioinformatics, of the existence as well as the source and availability of bioinformatics software, and how to apply these tools and interpret results with confidence. This process is aided by chapters that introduce important aspects of bioinformatics, detailed bioinformatics exercises (including solutions), and to cap it all, a glossary of definitions and terminology relating to bioinformatics. |
| differential gene expression analysis: Next Generation Sequencing Jerzy Kulski, 2016-01-14 Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences. |
| differential gene expression analysis: The Promise of Adolescence National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Division of Behavioral and Social Sciences and Education, Board on Children, Youth, and Families, Committee on the Neurobiological and Socio-behavioral Science of Adolescent Development and Its Applications, 2019-07-26 Adolescenceâ€beginning with the onset of puberty and ending in the mid-20sâ€is a critical period of development during which key areas of the brain mature and develop. These changes in brain structure, function, and connectivity mark adolescence as a period of opportunity to discover new vistas, to form relationships with peers and adults, and to explore one's developing identity. It is also a period of resilience that can ameliorate childhood setbacks and set the stage for a thriving trajectory over the life course. Because adolescents comprise nearly one-fourth of the entire U.S. population, the nation needs policies and practices that will better leverage these developmental opportunities to harness the promise of adolescenceâ€rather than focusing myopically on containing its risks. This report examines the neurobiological and socio-behavioral science of adolescent development and outlines how this knowledge can be applied, both to promote adolescent well-being, resilience, and development, and to rectify structural barriers and inequalities in opportunity, enabling all adolescents to flourish. |
| differential gene expression analysis: The Kiwifruit Genome Raffaele Testolin, Hong-Wen Huang, Allan Ross Ferguson, 2016-05-02 This book describes the basic botanical features of kiwifruit and its wild relatives, reports on the steps that led to its genome sequencing, and discusses the results obtained with the assembly and annotation. The core chapters provide essential insights into the main gene families that characterize this species as a crop, including the genes controlling sugar and starch metabolism, pigment biosynthesis and degradation, the ascorbic-acid pathway, fruit softening and postharvest metabolism, allergens, and resistance to pests and diseases. The book offers a valuable reference guide for taxonomists, geneticists and horticulturists. Further, since information gained from the genome sequence is extraordinarily useful in assessing the breeding value of individuals based on whole-genome scans, it will especially benefit plant breeders. Accordingly, chapters are included that focus on gene introgression from wild relatives and genome-based breeding. |
| differential gene expression analysis: Metabolic Pathway Engineering Michael E. Himmel, Yannick J. Bomble, 2020-07-28 This book illustrates experimental and computational methodologies used to achieve cost effective biological processes for the production of fuels and biochemicals through multiple approaches to increasing yield, titers, and productivity in a robust host. The volume includes the most recent and cutting-edge aspects of pathway engineering, flux analysis, and metabolic enzyme engineering. Each chapter highlights the complexity and challenges of the problem as well as the methods used to solve this problem or changes needed in current methods. As a part of the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that gives researchers a much needed boost. Authoritative and practical, Metabolic Pathway Engineering benefits not only scientists working on more fundamental aspects of this endeavor but also those in the biochemical industry working on strain engineering for robust industrial processes. |
| differential gene expression analysis: Advances in Visual Computing George Bebis, Zhaozheng Yin, Edward Kim, Jan Bender, Kartic Subr, Bum Chul Kwon, Jian Zhao, Denis Kalkofen, George Baciu, 2020-12-12 This two-volume set of LNCS 12509 and 12510 constitutes the refereed proceedings of the 15th International Symposium on Visual Computing, ISVC 2020, which was supposed to be held in San Diego, CA, USA in October 2020, took place virtually instead due to the COVID-19 pandemic. The 114 full and 4 short papers presented in these volumes were carefully reviewed and selected from 175 submissions. The papers are organized into the following topical sections: Part I: deep learning; segmentation; visualization; video analysis and event recognition; ST: computational bioimaging; applications; biometrics; motion and tracking; computer graphics; virtual reality; and ST: computer vision advances in geo-spatial applications and remote sensing Part II: object recognition/detection/categorization; 3D reconstruction; medical image analysis; vision for robotics; statistical pattern recognition; posters |
What exactly is a differential? - Mathematics Stack Exchange
Jul 13, 2015 · The differential of a function $f$ at $x_0$ is simply the linear function which produces the best linear approximation of $f(x)$ in a neighbourhood of $x_0$.
calculus - What is the practical difference between a differential …
See this answer in Quora: What is the difference between derivative and differential?. In simple words, the rate of change of function is called as a derivative and differential is the actual …
What is a differential form? - Mathematics Stack Exchange
Mar 4, 2020 · At this point, however, I think that the best way to approach the daunting concept of differential forms is to realize that differential forms are defined to be the thing that makes …
calculus - The second differential versus the differential of a ...
Jul 8, 2018 · Now if you want to, you can partially evaluate the second differential $ \mathrm d ^ 2 y $ when $ \mathrm d ^ 2 x = 0 $, getting a partial second differential showing only the …
Best Book For Differential Equations? - Mathematics Stack …
For mathematics departments, some more strict books may be suitable. But whatever book you are using, make sure it has a lot of solved examples. And ideally, it should also include some …
How To Solve a Trigonometric Differential Equation
Dec 23, 2018 · $\begingroup$ Well, I saw this equation in a fb group named JulioProfe some time ago. I found the exercise interesting and decided to take it back a few days ago, I don't know …
soft question - Differential topology versus differential geometry ...
Jul 6, 2015 · $\begingroup$ Differential topology deals with the study of differential manifolds without using tools related with a metric: curvature, affine connections, etc. Differential …
real analysis - Rigorous definition of "differential" - Mathematics ...
Nov 3, 2016 · Of course, defining $$ \mathrm{d}x= \lim_{\Delta x \to 0}\Delta x $$ is the same as defining $$ dx=0, $$ which makes no sense.
tensors - How to differentiate a differential form? - Mathematics …
Mar 18, 2013 · There is a formula of computing exterior derivative of any differential form (which is assumed to be smooth). In your case, if $\sigma$ is a 1-form, and $$ \sigma = \sum_{j=1}^n f_j …
"Differential" of a measure - Mathematics Stack Exchange
Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their …
What exactly is a differential? - Mathematics Stack Exchange
Jul 13, 2015 · The differential of a function $f$ at $x_0$ is simply the linear function which produces the best linear approximation of $f(x)$ in a neighbourhood of $x_0$.
calculus - What is the practical difference between a differential …
See this answer in Quora: What is the difference between derivative and differential?. In simple words, the rate of change of function is called as a derivative and differential is the actual …
What is a differential form? - Mathematics Stack Exchange
Mar 4, 2020 · At this point, however, I think that the best way to approach the daunting concept of differential forms is to realize that differential forms are defined to be the thing that makes …
calculus - The second differential versus the differential of a ...
Jul 8, 2018 · Now if you want to, you can partially evaluate the second differential $ \mathrm d ^ 2 y $ when $ \mathrm d ^ 2 x = 0 $, getting a partial second differential showing only the …
Best Book For Differential Equations? - Mathematics Stack Exchange
For mathematics departments, some more strict books may be suitable. But whatever book you are using, make sure it has a lot of solved examples. And ideally, it should also include some …
How To Solve a Trigonometric Differential Equation
Dec 23, 2018 · $\begingroup$ Well, I saw this equation in a fb group named JulioProfe some time ago. I found the exercise interesting and decided to take it back a few days ago, I don't know …
soft question - Differential topology versus differential geometry ...
Jul 6, 2015 · $\begingroup$ Differential topology deals with the study of differential manifolds without using tools related with a metric: curvature, affine connections, etc. Differential …
real analysis - Rigorous definition of "differential" - Mathematics ...
Nov 3, 2016 · Of course, defining $$ \mathrm{d}x= \lim_{\Delta x \to 0}\Delta x $$ is the same as defining $$ dx=0, $$ which makes no sense.
tensors - How to differentiate a differential form? - Mathematics …
Mar 18, 2013 · There is a formula of computing exterior derivative of any differential form (which is assumed to be smooth). In your case, if $\sigma$ is a 1-form, and $$ \sigma = \sum_{j=1}^n …
"Differential" of a measure - Mathematics Stack Exchange
Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their …
