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Gene Expression Data Analysis
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 biological sciences
R 9,332.32
Analysis of Gene Expression Data
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.
R 6,356.09
Gene Expression Data Analysis
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 biological sciences
R 3,287.91
Analysis of Gene Expression Data
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.
R 5,790.99
Microarray Gene Expression Data Analysis
This guide covers aspects of designing microarray experiments and analysing the data generated, including information on some of the tools that are available from non-commercial sources. Concepts and principles underpinning gene expression analysis are emphasised and wherever possible, the mathematics has been simplified. The guide is intended for use by graduates and researchers in bioinformatics and the life sciences and is also suitable for statisticians who are interested in the approaches currently used to study gene expression. Microarrays are an automated way of carrying out thousands of experiments at once, and allows scientists to obtain huge amounts of information very quicklyShort, concise text on this difficult topic areaClear illustrations throughoutWritten by well-known teachers in the subjectProvides insight into how to analyse the data produced from microarrays
R 3,516.00
Advanced Analysis Of Gene Expression Microarray Data
This book 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.Biomedical researchers will find this book invaluable for learning the cutting-edge methods for analyzing gene expression microarray data. Specifically, the coverage includes the following state-of-the-art methods:• Gene-based analysis: the latest novel clustering algorithms to identify co-expressed genes and coherent patterns in gene expression microarray data sets• Sample-based analysis: supervised and unsupervised methods for the reduction of the gene dimensionality to select significant genes. A series of approaches to disease classification and discovery are also described• Pattern-based analysis: methods for ascertaining the relationship between (subsets of) genes and (subsets of) samples. Various novel pattern-based clustering algorithms to find the coherent patterns embedded in the sub-attribute spaces are discussed• Visualization tools: various methods for gene expression data visualization. The visualization process is intended to transform the gene expression data set from high-dimensional space into a more easily understood two- or three-dimensional space.
R 3,928.93
Analysis of Microarray Gene Expression Data
After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.
R 5,946.06
Gene Expression Analysis
This second edition volume expands on the previous edition with updates on the latest methodologies in the transcriptomics field. The chapters in this book cover topics such as spatial omics, long-read sequencing technology, tissue microarrays, analysis of saliva and extracellular vesicles, machine learning and artificial intelligence-based approaches for analysis of singe cells transcriptome, and large sets of data on multi-omics including transcriptomics. 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.Cutting-edge and practical, Gene Expression Analysis: Methods and Protocols, Second Edition is a valuable resource for advanced undergraduate and graduate students studying gene expression analysis, and scientists interested in learning more about this rapidly advancing field.
R 10,793.43
Analysis of Microarray Gene Expression Data
After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.
R 4,669.57
Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R
Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline" of analytical methods with concrete examples starting from raw gene expression and DNA methylation data at the genome scale. Methods on quality control, data pre-processing, data mining, and further assessments are presented in the book, and R programs based on simulated data and real data are included. Codes with example data are all reproducible.Features:· Provides a sequence of analytical tools for genome-scale gene expression data and DNA methylation data, starting from quality control and pre-processing of raw genome-scale data.· Organized by a parallel presentation with explanation on statistical methods and corresponding R packages/functions in quality control, pre-processing, and data analyses (e.g., clustering and networks).· Includes source codes with simulated and real data to reproduce the results. Readers are expected to gain the ability to independently analyze genome-scaled expression and methylation data and detect potential biomarkers.This book is ideal for students majoring in statistics, biostatistics, and bioinformatics and researchers with an interest in high dimensional genetic and epigenetic studies.
R 4,326.37
Serial Analysis of Gene Expression (SAGE)
This book facilitates the introduction of SAGE into the laboratory and provides a framework for interpreting and comparing data derived from SAGE experiments. SAGE studies encompass 50,000 tags and can provide detailed knowledge of the 2000 most highly expressed genes in the tissue sample. The SAGE protocols presented are detailed, fully annotated, and tested, and are all written by experienced SAGE researchers from around the world.
R 4,076.08
Gene Expression Profiling
Leading scientists in gene expression methodology and bioinformatics data analysis describe readily reproducible methods for measuring RNA levels in cells and tissues. The techniques presented include new methods for applying the Affymetrix GeneChip®, SAR-SAGE, StaRT-PCR, SSH, the Invader Assay®, and ADGEM. The authors also provide critical bioinformatics insight and resources for data analysis and management. By distilling the basic underlying principles of many methods to a few straightforward concepts, investigators can easily choose the method most appropriate to their application.
R 4,655.55
Gene Expression Profiling
Leading scientists in gene expression methodology and bioinformatics data analysis describe readily reproducible methods for measuring RNA levels in cells and tissues. The techniques presented include new methods for applying the Affymetrix GeneChip®, SAR-SAGE, StaRT-PCR, SSH, the Invader Assay®, and ADGEM. The authors also provide critical bioinformatics insight and resources for data analysis and management. By distilling the basic underlying principles of many methods to a few straightforward concepts, investigators can easily choose the method most appropriate to their application.
R 3,399.07
Serial Analysis of Gene Expression (SAGE)
This book facilitates the introduction of SAGE into the laboratory and provides a framework for interpreting and comparing data derived from SAGE experiments. SAGE studies encompass 50,000 tags and can provide detailed knowledge of the 2000 most highly expressed genes in the tissue sample. The SAGE protocols presented are detailed, fully annotated, and tested, and are all written by experienced SAGE researchers from around the world.
R 3,024.09
Gene Expression Profiling
Understanding gene expression and how it changes under normal and pathological conditions is essential to our understanding of the fundamentals of cell biology through to the targeted treatment of disease. In Gene Expression Profiling: Methods and Protocols, Second Edition, experts in their particular fields compile detailed protocols for a broad range of techniques, currently available and being further developed, for the analysis of gene expression at the DNA, RNA, and protein levels. 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. Practical and easy-to-use, Gene Expression Profiling: Methods and Protocols, Second Edition presents a collection of clearly described and illustrated chapters, certain to be helpful to researchers in academia, in hospitals, and in industry who are interested in applying techniques, whether basic or advanced, for the analysis of gene expression.
R 2,931.00
Weighted Network Analysis
High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis strategies. Gene co-expression networks have been used to study a variety of biological systems, bridging the gap from individual genes to biologically or clinically important emergent phenotypes.
R 6,973.75
Genomics Data Analysis
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
R 1,246.00
Weighted Network Analysis
High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis strategies. Gene co-expression networks have been used to study a variety of biological systems, bridging the gap from individual genes to biologically or clinically important emergent phenotypes.
R 6,944.77
R 2,936.96
Primer to Analysis of Genomic Data Using R
Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection, population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data. At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto< tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher’s website.
R 2,626.48
Dna Microarray Technology And Data Analysis In Cancer Research
DNA microarray technology has become a useful technique in gene expression analysis for the development of new diagnostic tools and for the identification of disease genes and therapeutic targets for human cancers. Appropriate control for DNA microarray experiment and reliable analysis of the array data are key to performing the assay and utilizing the data correctly. The most difficult challenge has been the lack of a powerful method to analyze the data for all genes (more than 30,000 genes) simultaneously and to use the microarray data in a decision-making process. In this book, the authors describe DNA microarray technology and data analysis by pointing out current advantages and disadvantages of the technique and available analytical methods. Crucially, new ideas and analytical methods based on the authors' own experience in DNA microarray study and analysis are introduced. It is believed that this new way of interpreting and analyzing microarray data will bring us closer to success in decision-making using the information obtained through the DNA microarray technology.
R 3,418.03
Practical Approach to Microarray Data Analysis
In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships.
R 1,569.73
Design and Analysis of DNA Microarray Investigations
The analysis of gene expression profile data from DNA micorarray studies are discussed in this book. It provides a review of available methods and presents it in a manner that is intelligible to biologists. It offers an understanding of the design and analysis of experiments utilizing microarrays to benefit scientists. It includes an Appendix tutorial on the use of BRB-ArrayTools and step by step analyses of several major datasets using this software which is available from the National Cancer Institute.
R 3,517.70
Gene Function Analysis
The determination of protein function has been a major goal of molecular biology since the founding of the discipline. However, as we learn more about gene function, we discover that the context within which a gene is expressed controls the specific function of that gene. It has become critical to establish the background in which gene function is determined and to perform experiments in multiple applicable backgrounds. In Gene Function Analysis, Second Edition, a number of computational and experimental techniques are presented for identifying not only the function of an individual gene, but also the partners that work with that gene. The theme of data integration runs strongly through the computational techniques, with many focusing on gathering data from different sources and different biomolecular types. Experimental techniques have evolved to determine function in specific tissues and at specific times during development. Written in the 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 protocols, and notes on troubleshooting and avoiding known pitfalls.Authoritative and easily accessible, Gene Function Analysis, Second Edition seeks to serve both professionals and novices with a growing understanding of the complexity of gene function.
R 5,991.64
Methods of Microarray Data Analysis IV
As studies using microarray technology have evolved, so have the data analysis methods used to analyze these experiments. The CAMDA conference plays a role in this evolving field by providing a forum in which investors can analyze the same data sets using different methods. Methods of Microarray Data Analysis IV is the fourth book in this series, and focuses on the important issue of associating array data with a survival endpoint. Previous books in this series focused on classification (Volume I), pattern recognition (Volume II), and quality control issues (Volume III).In this volume, four lung cancer data sets are the focus of analysis. We highlight three tutorial papers, including one to assist with a basic understanding of lung cancer, a review of survival analysis in the gene expression literature, and a paper on replication. In addition, 14 papers presented at the conference are included. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state of the art of microarray data analysis.Jennifer Shoemaker is a faculty member in the Department of Biostatistics and Bioinformatics and the Director of the Bioinformatics Unit for the Cancer and Leukemia Group B Statistical Center, Duke University Medical Center. Simon Lin is a faculty member in the Department of Biostatistics and Bioinformatics and the Manager of the Duke Bioinformatics Shared Resource, Duke University Medical Center.
R 4,030.00
DNA Microarrays and Gene Expression
Massive data acquisition technologies, such as genome sequencing, high-throughput drug screening, and DNA arrays are in the process of revolutionizing biology and medicine. Using the mRNA of a given cell, at a given time, under a given set of conditions, DNA microarrays can provide a snapshot of the level of expression of all the genes in the cell. Such snapshots can be used to study fundamental biological phenomena such as development or evolution, to determine the function of new genes, to infer the role individual genes or groups of genes may play in diseases, and to monitor the effect of drugs and other compounds on gene expression. Originally published in 2002, this inter-disciplinary introduction to DNA arrays will be of value to anyone with an a interest in this powerful technology.
R 1,967.42
Practical Approach to Microarray Data Analysis
In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships.
R 1,570.63
Gene Expression and Its Discontents
This book describes how epigenetic context, in a large sense, affects gene expression and the development of an organism, using the asymptotic limit theorems of information theory to construct statistical models useful in data analysis. The approach allows deep understanding of how embedding context affects development. We find that epigenetic information sources act as tunable catalysts, directing ontogeny into characteristic pathways, a perspective having important implications for epigenetic epidemiology. In sum, environmental stressors can induce a broad spectrum of developmental dysfunctions, and the book explores a number of pandemic chronic diseases, using U.S. data at different scales and levels of organization. In particular, we find the legacy of slavery has been grossly compounded by accelerating industrial decline and urban decay. Individual chapters are dedicated to obesity and its sequelae, coronary heart disease, cancer, mental disorders, autoimmune dysfunction, Alzheimer’s disease, and other conditions. Developmental disorders are driven by environmental factors channeled by historical trajectory and are unlikely to respond to medical interventions at the population level in the face of persistent individual and community stress. Drugs powerful enough to affect deleterious epigenetic programming will likely have side effects leading to shortened lifespan. Addressing chronic conditions and developmental disorders requires significant large-scale changes in public policy and resource allocation.
R 4,663.11
Meta-analysis and Combining Information in Genetics and Genomics
Novel Techniques for Analyzing and Combining Data from Modern Biological StudiesBroadens the Traditional Definition of Meta-AnalysisWith the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods, Meta-analysis and Combining Information in Genetics and Genomics looks at how to analyze multiple studies from a broad perspective.After presenting the basic ideas and tools of meta-analysis, the book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types, giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations.As heterogeneous data sets become more common, biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources.
R 4,470.04
Methods of Microarray Data Analysis IV
As studies using microarray technology have evolved, so have the data analysis methods used to analyze these experiments. The CAMDA conference plays a role in this evolving field by providing a forum in which investors can analyze the same data sets using different methods. Methods of Microarray Data Analysis IV is the fourth book in this series, and focuses on the important issue of associating array data with a survival endpoint. Previous books in this series focused on classification (Volume I), pattern recognition (Volume II), and quality control issues (Volume III).In this volume, four lung cancer data sets are the focus of analysis. We highlight three tutorial papers, including one to assist with a basic understanding of lung cancer, a review of survival analysis in the gene expression literature, and a paper on replication. In addition, 14 papers presented at the conference are included. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state of the art of microarray data analysis.Jennifer Shoemaker is a faculty member in the Department of Biostatistics and Bioinformatics and the Director of the Bioinformatics Unit for the Cancer and Leukemia Group B Statistical Center, Duke University Medical Center. Simon Lin is a faculty member in the Department of Biostatistics and Bioinformatics and the Manager of the Duke Bioinformatics Shared Resource, Duke University Medical Center.
R 3,932.48
Epigenetics and Gene Expression in Cancer, Inflammatory and Immune Diseases
This practical collection examines methodologies originating from the benefits of genome-wide approaches to studying epigenetics, which has opened the emerging field of epigenomics. Focusing on the areas of cancer, inflammatory and autoimmune disorders, chapters discuss three main components of the epigenome and their role in the regulation of gene expression and present a detailed method section specific to studying each component, including data analyses, troubleshooting, and feasibility in different experimental settings. The main topics are high-throughput and targeted methods for DNA methylation analysis, nucleosome position mapping, studying epigenetic effects of gut microbiota, optical imaging for detection of epigenetic aberrations in living cells, methods for microRNA, and histone code profiling. Written for the Methods in Pharmacology and Toxicology series, the book includes the kind of detail and implementation advice to encourage success in the lab. Authoritative and easily applicable, Epigenetics and Gene Expression in Cancer, Inflammatory and Immune Diseases aims to provide pharmacologists, molecular biologists, bioinformaticians, and toxicologists with a vital background on epigenetics and state-of-the-art techniques in epigenomics.
R 3,328.17
Transcriptome Data Analysis
This detailed volume provides comprehensive practical guidance on transcriptome data analysis for a variety of scientific purposes. Beginning with general protocols, the collection moves on to explore protocols for gene characterization analysis with RNA-seq data as well as protocols on several new applications of transcriptome studies. 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 useful, Transcriptome Data Analysis: Methods and Protocols serves as an ideal guide to the expanding purposes of this field of study.
R 6,937.23
DNA Microarrays: Gene Expression Applications
This complete and practical manual on expression measurement using DNA arrays covers the existing methods (from nylon macroarrays to oligonucleotide chips) and includes detailed protocols. It has been written by practising scientists who have experienced the difficulties involved in actually using microarrays, and provides helpful advice and hints on setting up these powerful but sometimes tricky methods. Software, data mining procedures and probable future developments, which should be useful to any practising scientist interested in expression measurement, are also covered in this book.
R 2,027.09
Cardiac Gene Expression
This book presents both cutting-edge and established methods for studying cardiac gene expression. The protocols provide a template for solid research, and cover the process through screening, analysis, characterization, and functional confirmation of novel genes or known genes with a new function. The concluding section of the book highlights methods that facilitate overexpression or cardiac-specific targeted gene deletion.
R 3,855.26
Gene Expression
This book is the first volume in a new series Progress in Gene Expres sion. The control of gene expression is a central-most topic in molecular biology as it deals with the utilization and regulation of gene informa tion. As we see huge efforts mounting all over the developed world to understand the structure and organization of several complex eukaryotic genomes in the form of Gene Projects and Genome Centers, we have to remember that without understanding the basic mechanisms that gov ern the use of genetic information, much of this effort will not be very productive. Fortunately, however, research during the past seven years on the mechanisms that control gene expression in eukaryotes has been extremely successful in generating a wealth of information on the basic strategies of transcriptional control. (Although regulation of gene ex pression is exerted at many different levels, much of the emphasis in this series will be on transcriptional control. A future volume, however, will deal with other levels of regulation). The progress in understanding the control of eukaryotic transcription can only be appreciated by realizing that seven years ago we did not know the primary structure of a single sequence specific transcriptional activator, and those whose primary structures were available (e. g. , homeo domain proteins) were not yet recognized to function in this capacity.
R 1,558.72
Epigenetics and Gene Expression in Cancer, Inflammatory and Immune Diseases
This practical collection examines methodologies originating from the benefits of genome-wide approaches to studying epigenetics, which has opened the emerging field of epigenomics. Focusing on the areas of cancer, inflammatory and autoimmune disorders, chapters discuss three main components of the epigenome and their role in the regulation of gene expression and present a detailed method section specific to studying each component, including data analyses, troubleshooting, and feasibility in different experimental settings. The main topics are high-throughput and targeted methods for DNA methylation analysis, nucleosome position mapping, studying epigenetic effects of gut microbiota, optical imaging for detection of epigenetic aberrations in living cells, methods for microRNA, and histone code profiling. Written for the Methods in Pharmacology and Toxicology series, the book includes the kind of detail and implementation advice to encourage success in the lab. Authoritative and easily applicable, Epigenetics and Gene Expression in Cancer, Inflammatory and Immune Diseases aims to provide pharmacologists, molecular biologists, bioinformaticians, and toxicologists with a vital background on epigenetics and state-of-the-art techniques in epigenomics.
R 3,672.94
Machine Learning in Single-Cell RNA-seq Data Analysis
This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of cellular diversity and function, offering unprecedented insights into the intricate tapestry of gene expression at the single-cell level. However, the deluge of data generated by these experiments presents a formidable challenge, demanding advanced analytical tools, methodologies, and skills for meaningful interpretation. This book bridges the gap between traditional bioinformatics and the evolving landscape of machine learning. Authored by seasoned experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets.
R 2,494.11
Gene Expression Profiling
Understanding gene expression and how it changes under normal and pathological conditions is essential to our understanding of the fundamentals of cell biology through to the targeted treatment of disease. In Gene Expression Profiling: Methods and Protocols, Second Edition, experts in their particular fields compile detailed protocols for a broad range of techniques, currently available and being further developed, for the analysis of gene expression at the DNA, RNA, and protein levels. 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. Practical and easy-to-use, Gene Expression Profiling: Methods and Protocols, Second Edition presents a collection of clearly described and illustrated chapters, certain to be helpful to researchers in academia, in hospitals, and in industry who are interested in applying techniques, whether basic or advanced, for the analysis of gene expression.
R 4,788.82
Microarray Data Analysis
This volume covers a large area, from the description of methodologies for data analysis to the real application. Chapters focus on methodologies for preprocessing of microarray data, a survey of miRNA Data analysis, Cloud-based approaches, application of data mining techniques for data analysis, biclustering to query different datasets, web-based tool to analyze the evolution of miRNA clusters, application of biclustering to mine patterns of co-regulated genes ontologies, microarray and proteomic Data, Gene Regulatory Network Inference, Gene Regulatory Network methods, analysis of Mouse data for metabolomics studies, analysis of microRNA data in Multiple Myeloma, microarray data analysis in Gliobastomas, and microRNA data in Cardiogenesis.Written for the 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, Microarray Data Analysis: Methods and Applications, Second Edition aims to ensure successful results in the further study of this vital field.
R 6,822.79
RNA Abundance Analysis
This updated book covers a wide range of techniques on RNA extraction, detection, quantification, visualization, and genome-wide profiling, from conventional methods to state-of-the-art high throughput approaches. It includes detailed techniques to examine mRNAs, small non-coding RNAs, protein-associated small RNAs, organelle RNAs, endosymbiont RNAs, and alternatively spliced RNA variants from various organisms. RNA editing and the computational data processing for genome-wide datasets is also discussed. 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, RNA Abundance Analysis: Methods and Protocols, Second Edition provides a comprehensive set of techniques and methods on isolating and analyzing mRNAs, small RNAs, and other RNA variants, in order to assist researchers in their gene expression studies.
R 4,396.00
Gene Expression Programming
This book describes the basic ideas of gene expression programming (GEP) and numerous modifications to this powerful new algorithm. It provides all the implementation details of GEP so that anyone with elementary programming skills will be able to implement it themselves. The book includes a self-contained introduction to this new exciting field of computational intelligence. This second edition has been revised and extended with five new chapters.
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Gene Mapping, Discovery, and Expression
Completion of the sequence of the human genome represents an unpar- leled achievement in the history of biology. The project has produced nearly complete, highly accurate, and comprehensive sequences of genomes of s- eral organisms including human, mouse, drosophila, and yeast. Furthermore, the development of high-throughput technologies has led to an explosion of projects to sequence the genomes of additional organisms including rat, chimp, dog, bee, chicken, and the list is expanding. The nearly completed draft of genomic sequences from numerous species has opened a new era of research in biology and in biomedical sciences. In keeping with the interdisciplinary nature of the new scientific era, the chapters in Gene Mapping, Discovery, and Expression: Methods and Protocols recapitulate the necessity of integration of experimental and computational tools for solving - portant research problems. The general underlying theme of this volume is DNA sequence-based technologies. At one level, the book highlights the importance of databases, genome-browsers, and web-based tools for data access and ana- sis. More specifically, sequencing projects routinely deposit their data in p- licly available databases including GenBank, at the National Center of Biotechnology (NCBI) in the United States; EMBL, maintained by the European Bioinformatics Institute; and DDBJ, the DNA Data Bank of Japan. Currently, several browsers offer facile access to numerous genomic DNA sequences for gene mapping and data retrieval.
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Microarray Data Analysis
This volume covers a large area, from the description of methodologies for data analysis to the real application. Chapters focus on methodologies for preprocessing of microarray data, a survey of miRNA Data analysis, Cloud-based approaches, application of data mining techniques for data analysis, biclustering to query different datasets, web-based tool to analyze the evolution of miRNA clusters, application of biclustering to mine patterns of co-regulated genes ontologies, microarray and proteomic Data, Gene Regulatory Network Inference, Gene Regulatory Network methods, analysis of Mouse data for metabolomics studies, analysis of microRNA data in Multiple Myeloma, microarray data analysis in Gliobastomas, and microRNA data in Cardiogenesis.Written for the 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, Microarray Data Analysis: Methods and Applications, Second Edition aims to ensure successful results in the further study of this vital field.
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