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Bayesian Methods For High Throughput Gene Expression Data In Bioinformatics
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Book Synopsis Bayesian Analysis of Gene Expression Data by : Bani K. Mallick
Download or read book Bayesian Analysis of Gene Expression Data written by Bani K. Mallick and published by John Wiley & Sons. This book was released on 2009-07-20 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable. This book: Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data. Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications. Accompanied by website featuring datasets, exercises and solutions. Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.
Book Synopsis Bayesian Methods for High-throughput Gene Expression Data in Bioinformatics by : Fang Yu
Download or read book Bayesian Methods for High-throughput Gene Expression Data in Bioinformatics written by Fang Yu and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Inference for Gene Expression and Proteomics by : Kim-Anh Do
Download or read book Bayesian Inference for Gene Expression and Proteomics written by Kim-Anh Do and published by Cambridge University Press. This book was released on 2006-07-24 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
Book Synopsis Bayesian Modeling in Bioinformatics by : Dipak K. Dey
Download or read book Bayesian Modeling in Bioinformatics written by Dipak K. Dey and published by CRC Press. This book was released on 2010-09-03 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and c
Book Synopsis Bayesian Methods for Gene Expression Analysis from High-throughput Sequencing Data by : Peter Glaus
Download or read book Bayesian Methods for Gene Expression Analysis from High-throughput Sequencing Data written by Peter Glaus and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Statistical Bioinformatics by : Kim-Anh Do
Download or read book Advances in Statistical Bioinformatics written by Kim-Anh Do and published by Cambridge University Press. This book was released on 2013-06-10 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation.
Book Synopsis Handbook of Statistical Bioinformatics by : Henry Horng-Shing Lu
Download or read book Handbook of Statistical Bioinformatics written by Henry Horng-Shing Lu and published by Springer Science & Business Media. This book was released on 2011-05-17 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.
Book Synopsis The Analysis of Gene Expression Data by : Giovanni Parmigiani
Download or read book The Analysis of Gene Expression Data written by Giovanni Parmigiani and published by Springer Science & Business Media. This book was released on 2006-04-11 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Bayesian Learning in Bioinformatics by : David L. Gold
Download or read book Bayesian Learning in Bioinformatics written by David L. Gold and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Life sciences research is advancing in breadth and scope, affecting many areas of life including medical care and government policy. The field of Bioinformatics, in particular, is growing very rapidly with the help of computer science, statistics, applied mathematics, and engineering. New high-throughput technologies are making it possible to measure genomic variation across phenotypes in organisms at costs that were once inconceivable. In conjunction, and partly as a consequence, massive amounts of information about the genomes of many organisms are becoming accessible in the public domain. Some of the important and exciting questions in the post-genomics era are how to integrate all of the information available from diverse sources. Learning in complex systems biology requires that information be shared in a natural and interpretable way, to integrate knowledge and data. The statistical sciences can support the advancement of learning in Bioinformatics in many ways, not the least of which is by developing methodologies that can support the synchronization of efforts across sciences, offering real-time learning tools that can be shared across many fields from basic science to the clinical applications. This research is an introduction to several current research problems in Bioinformatics that addresses integration of information, and discusses statistical methodologies from the Bayesian school of thought that may be applied. Bayesian statistical methodologies are proposed to integrate biological knowledge and improve statistical inference for three relevant Bioinformatics applications: gene expression arrays, BAC and aCGH arrays, and real-time gene expression experiments. A unified Bayesian model is proposed to perform detection of genes and gene classes, defined from historical pathways, with gene expression arrays. A novel Bayesian statistical method is proposed to infer chromosomal copy number aberrations in clinical populations with BAC or aCGH experiments. A theoretical model is proposed, motivated from historical work in mathematical biology, for inference with real-time gene expression experiments, and fit with Bayesian methods. Simulation and case studies show that Bayesian methodologies show great promise to improve the way we learn with high-throughput Bioinformatics experiments.
Book Synopsis Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics by : Raphaël Mourad
Download or read book Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics written by Raphaël Mourad and published by OUP Oxford. This book was released on 2014-09-18 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.
Book Synopsis Integrative Cluster Analysis in Bioinformatics by : Basel Abu-Jamous
Download or read book Integrative Cluster Analysis in Bioinformatics written by Basel Abu-Jamous and published by John Wiley & Sons. This book was released on 2015-06-15 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review of clustering analysis in bioinformatics from the fundamentals through to state-of-the-art techniques and applications. Key Features: Offers a contemporary review of clustering methods and applications in the field of bioinformatics, with particular emphasis on gene expression analysis Provides an excellent introduction to molecular biology with computer scientists and information engineering researchers in mind, laying out the basic biological knowledge behind the application of clustering analysis techniques in bioinformatics Explains the structure and properties of many types of high-throughput datasets commonly found in biological studies Discusses how clustering methods and their possible successors would be used to enhance the pace of biological discoveries in the future Includes a companion website hosting a selected collection of codes and links to publicly available datasets
Book Synopsis Knowledge-Based Bioinformatics by : Gil Alterovitz
Download or read book Knowledge-Based Bioinformatics written by Gil Alterovitz and published by John Wiley & Sons. This book was released on 2011-04-20 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine. Key Features: Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology. Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions. Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics. Written by leading international experts in this field. Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.
Book Synopsis High-Dimensional Data Analysis in Cancer Research by : Xiaochun Li
Download or read book High-Dimensional Data Analysis in Cancer Research written by Xiaochun Li and published by Springer Science & Business Media. This book was released on 2008-12-19 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.
Book Synopsis Statistical Analysis of Next Generation Sequencing Data by : Somnath Datta
Download or read book Statistical Analysis of Next Generation Sequencing Data written by Somnath Datta and published by Springer. This book was released on 2014-07-03 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Big Data Analytics in Bioinformatics and Healthcare by : Wang, Baoying
Download or read book Big Data Analytics in Bioinformatics and Healthcare written by Wang, Baoying and published by IGI Global. This book was released on 2014-10-31 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.
Book Synopsis Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics by : Benjamin Haibe-Kains
Download or read book Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics written by Benjamin Haibe-Kains and published by Frontiers Media SA. This book was released on 2015-04-14 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput “omics” technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the major challenges of the post-genomic era is to extract functional information by integrating such heterogeneous high-throughput genomic data. This is not a trivial task as we are increasingly coming to understand that it is not individual genes, but rather biological pathways and networks that drive an organism’s response to environmental factors and the development of its particular phenotype. In order to fully understand the way in which these networks interact (or fail to do so) in specific states (disease for instance), we must learn both, the structure of the underlying networks and the rules that govern their behavior. In recent years there has been an increasing interest in methods that aim to infer biological networks. These methods enable the opportunity for better understanding the interactions between genomic features and the overall structure and behavior of the underlying networks. So far, such network models have been mainly used to identify and validate new interactions between genes of interest. But ultimately, one could use these networks to predict large-scale effects of perturbations, such as treatment by multiple targeted drugs. However, currently, we are still at an early stage of comprehending methods and approaches providing a robust statistical framework to quantitatively assess the quality of network inference and its predictive potential. The scope of this Research Topic in Bioinformatics and Computational Biology aims at addressing these issues by investigating the various, complementary approaches to quantify the quality of network models. These “validation” techniques could focus on assessing quality of specific interactions, global and local structures, and predictive ability of network models. These methods could rely exclusively on in silico evaluation procedures or they could be coupled with novel experimental designs to generate the biological data necessary to properly validate inferred networks.
Book Synopsis Bayesian Hierarchical Modeling of High-throughput Genomic Data with Applications to Cancer Bioinformatics and Stem Cell Differentiation by :
Download or read book Bayesian Hierarchical Modeling of High-throughput Genomic Data with Applications to Cancer Bioinformatics and Stem Cell Differentiation written by and published by . This book was released on 2015 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in the ability to obtain genomic measurements have continually outpaced advances in the ability to interpret them in a statistically rigorous manner. In this dissertation, I develop, evaluate, and apply Bayesian hierarchical modeling frameworks to uncover novel insights in cancer bioinformatics as well as explore and characterize stem cell expression heterogeneity. The first framework integrates diverse sets of genomic information to identify cancer patient subgroups. The recently developed survLDA (survival-supervised latent Dirichlet allocation) model is able to capture patient heterogeneity as well as incorporate many diverse data types, but the potential in utilizing the model for predictive inference has yet to be explored. This is evaluated empirically and under simulation studies to show that in order to accurately identify patient subgroups, the necessary sample size depends on the size of the model being used (number of topics), the size of each patient's document, and the number of patients considered. The second framework is a Model-based Approach for identifying Driver Genes in Cancer (MADGiC), which infers causal genes in cancer based on somatic mutation profiles. The model takes advantage of external data sources regarding background mutation rates and the potential for specific mutations to result in functional consequences. In addition, it leverages information about key mutational patterns that are typical of driver genes. As such, MADGiC encodes valuable prior information in a novel manner and incorporates several key sources of information that were previously only considered in isolation. This results in improved inference of driver genes, as demonstrated in simulation and case studies. Finally, the third framework identifies genes that exhibit differential regulation of expression at the single-cell level. Specifically, it is known that gene expression often occurs in a stochastic, bursty manner. When profiling across many cells, these bursty gene expression patterns may be exhibited by multimodal distributions. Identifying these bursty expression patterns as well as detecting differences across biological conditions, which may represent differential regulation, is an important first step in many single-cell experiments. We develop a Bayesian nonparametric mixture modeling approach that explicitly accounts for these multimodal patterns and demonstrate its utility using simulation and case studies.
