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Sparse Model Building From Genome Wide Variation With Graphical Models
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Book Synopsis Sparse Model Building from Genome-wide Variation with Graphical Models by : Benjamin Alexander Logsdon
Download or read book Sparse Model Building from Genome-wide Variation with Graphical Models written by Benjamin Alexander Logsdon and published by . This book was released on 2011 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: High throughput sequencing and expression characterization have lead to an explosion of phenotypic and genotypic molecular data underlying both experimental studies and outbred populations. We develop a novel class of algorithms to reconstruct sparse models among these molecular phenotypes (e.g. expression products) and genotypes (e.g. single nucleotide polymorphisms), via both a Bayesian hierarchical model, when the sample size is much smaller than the model dimension (i.e. p n) and the well characterized adaptive lasso algo- rithm. Specifically, we propose novel approaches to the problems of increasing power to detect additional loci in genome-wide association studies using our variational algorithm, efficiently learning directed cyclic graphs from expression and genotype data using the adaptive lasso, and constructing genomewide undirected graphs among genotype, expression and downstream phenotype data using an extension of the variational feature selection algorithm. The Bayesian hierarchical model is derived for a parametric multiple regression model with a mixture prior of a point mass and normal distribution for each regression coefficient, and appropriate priors for the set of hyperparameters. When combined with a probabilistic consistency bound on the model dimension, this approach leads to very sparse solutions without the need for cross validation. We use a variational Bayes approximate inference approach in our algorithm, where we impose a complete factorization across all parameters for the approximate posterior distribution, and then minimize the KullbackLeibler divergence between the approximate and true posterior distributions. Since the prior distribution is non-convex, we restart the algorithm many times to find multiple posterior modes, and combine information across all discovered modes in an approximate Bayesian model averaging framework, to reduce the variance of the posterior probability estimates. We perform analysis of three major publicly available data-sets: the HapMap 2 genotype and expression data collected on immortalized lymphoblastoid cell lines, the genome-wide gene expression and genetic marker data collected for a yeast intercross, and genomewide gene expression, genetic marker, and downstream phenotypes related to weight in a mouse F2 intercross. Based on both simulations and data analysis we show that our algorithms can outperform other state of the art model selection procedures when including thousands to hundreds of thousands of genotypes and expression traits, in terms of aggressively controlling false discovery rate, and generating rich simultaneous statistical models.
Book Synopsis Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics by : Christine Sinoquet
Download or read book Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics written by Christine Sinoquet and published by OUP Oxford. This book was released on 2014-09-18 with total page 415 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 Sparse Model Learning for Identifying Nucleotide Motifs and Inferring Genotype and Phenotype Associations by : Indika Priyantha Kuruppu Appuhamilage
Download or read book Sparse Model Learning for Identifying Nucleotide Motifs and Inferring Genotype and Phenotype Associations written by Indika Priyantha Kuruppu Appuhamilage and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Variation in gene expression is an important mechanism underlying phenotypic variation in morphological, physiological and behavioral traits as well as disease susceptibility. A connection between DNA variants and gene expression levels not only provides more understanding of the biological network, but also enhances the mapping of these quantitative traits. Thus, an understanding of the mechanism of gene expression and the genotype/phenotype relationship is of paramount importance to both scientific research and social economics. The primary functionality of the gene expression process is to convert information stored in genes into gene products such as RNAs or proteins. The fundamental of this complex process is controlled by a class of proteins known as transcription factors (TFs) that bind to special locations of the DNA double helix. These special binding sites, known as transcription factor binding sites (TFBSs), are generally short motifs of 6-20 base pairs. Furthermore, the discovery of new TFBSs will contribute to the establishment of gene regulation networks, diagnosis of genetic diseases and new drug design. On the other hand, the genotype/phenotype relationship is mainly explained by multiple quantitative trait loci (QTLs), epistatic effects and environmental factors. A QTL is a section of DNA that correlates with variation in a phenotype. The QTL typically is linked to, or contains, the genes that control that phenotype interactions among QTLs or between genes, and environmental factors contribute substantially to variation in complex traits. During the last two decades the use of QTLs has proven to be effective for increasing food production, resistance to diseases and pests, tolerance to heat, cold and draught, and to improve nutrient content in animal and plant breeding. Therefore, the objective of this dissertation is to develop sparse models for such high dimensional data, develop accurate sparse variable selection and estimation algorithms for the models and design statistical methods for robust hypothesis tests for the TFBSs identification and QTL mapping problems. Although the sparse model learning works presented in this thesis are used in the context of TFBSs identification or QTL mapping problems, the algorithms are equally applicable to a broad range of problems, such as whole-genome QTL mapping and pathway-based genome-wide association study (GWAS), etc. The widely used computational methods for identifying TFBSs based on the position weight matrix (PWM) assume that the nucleotides at different positions of the TFBSs are independent. However, several experimental results demonstrate the dependencies among different positions. Recently, Bayesian networks (BN) and variable order Bayesian networks (VOBN) were proposed to model such dependencies and thereby improve the accuracy of predicting TFBSs. However, BN and VOBN model the dependencies in a directional manner, which may hinder their capability of completely capturing complex dependencies. To this end, we develop a Markov random field (MRF) based model for TFBSs capable of capturing complex unidirectional relationships among motifs. To capture the large extent of dependencies in a sparse model without causing overfitting, we develop a feature selection method that carefully chooses only the most relevant features of the model. An exhaustive simulation study affirmed that our MRF-based method outperforms other state-of-the-art methods based on VOBN. To further reduce the computational complexity of our algorithm, we introduce a novel pairwise MRF model to the TFBSs, and develop a fast algorithm to learn the model parameters. Specifically, we adopt an optimization method that employs the log determinant relaxation approach to evaluate the partition function in the MRF, which dramatically reduces the computational complexity of the algorithm. For the genotype/phenotype association problem, we develop a novel empirical Bayesian least absolute shrinkage and selection operator (EBlasso) algorithm with normal and exponential (NE) and normal, exponential and gamma (NEG) hierarchical prior distributions. Both of these algorithms employ a novel proximal gradient approach to simultaneously estimate model parameters that leads to extremely fast convergence. Furthermore, we develop a novel proximal gradient hybrid model capable of detecting more QTLs than its vanilla flavor, but still maintaining a lower false positive rate. Having both covariance and posterior modes estimated, they also provide a statistical testing method that considers as much information as possible without increasing the degrees of freedom (DF). Extensive simulation studies are carried out to evaluate the performance of the proposed methods, and real datasets are analyzed for validation. Both simulation and real data analyses suggest that the new methods are fast and accurate genotype-phenotype association methods that can easily handle high dimensional data, including possible main and interaction effects with orders of magnitude faster than existing state-of-the-art methods. Specifically, with the EBlasso-NEG, our new algorithm could easily handle more than [10]^5 possible effects within few seconds running on an average personal computer. Given the fundamental importance of gene expression and genotype/phenotype associations in understanding the genetic basis of complex biological system, the MRF, pairwise-MRF, EBlasso-NE, EBlasso-NEG and EBlasso-NEG hybrid algorithms and software packages developed in this dissertation achieve the effectiveness, robustness and efficiency needed for successful application to biology. With the advancement of high-throughput molecular technologies in generating information at genetic, epigenetic, transcriptional and posttranscriptional levels, the methods developed here have broad applications to infer TFBSs and different types of genotype and phenotypes associations.
Book Synopsis Sparse Model Learning for Inferring Genotype and Phenotype Associations by : Anhui Huang
Download or read book Sparse Model Learning for Inferring Genotype and Phenotype Associations written by Anhui Huang and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Genotype and phenotype associations are of paramount importance in understanding the genetic basis of living organisms, improving traits of interests in animal and plant breeding, as well as gaining insights into complex biological systems and the etiology of human diseases. With the advancements in molecular biology such as microarrays, high throughput next generation sequencing, RNAseq, et al, the number of available genotype markers is far exceeding the number of available samples in association studies. The objective of this dissertation is to develop sparse models for such high dimensional data, develop accurate sparse variable selection and estimation algorithms for the models, and design statistical methods for robust hypothesis tests for the genotype and phenotype associations. We develop a novel empirical Bayesian least absolute shrinkage and selection operator (EBlasso) algorithm with Normal, Exponential and Gamma (NEG), and Normal, Exponential (NE) hierarchical prior distributions, and an empirical Bayesian elastic net (EBEN) algorithm with an innovative Normal and generalized Gamma (NG) hierarchical prior distribution, for both general linear and generalized logistic regression models. Both of the two empirical Bayes methods estimate variance components of the regression coefficients with closed-form solutions and perform automatic variable selection such that a variable with zero variance is excluded from the model. With the closed-form solutions for variance components in the model and without estimating the posterior modes for excluded variables, the two empirical Bayes methods infer sparse models efficiently. Having both covariance and posterior modes estimated, they also provide a statistical testing method that considers as much information as possible without increasing the degrees of freedom (DF). Extensive simulation studies are carried out to evaluate the performance of the proposed methods, and real datasets are analyzed for validation. Both simulation and real data analyses suggest that the two methods are fast and accurate genotype-phenotype association methods that can easily handle high dimensional data including possible main and interaction effects. Comparing the two methods, EBlasso typically selects one variable out of a group of highly correlated effects, and the EBEN algorithm encourages a grouping effect that selects a group of effects if they are correlated. Not only verificatory simulation and real dataset analyses are performed, we further demonstrate the advantage of the developed algorithms through two exploratory applications, namely the whole-genome QTL mapping for an elite rice hybrid and pathway-based genome wide association study (GWAS) for human Parkinson disease (PD). In the first application, we exploit whole-genome markers of an immortalized F2 population derived from an elite rice hybrid to perform QTL mapping for the rice-yield phenotype. Our QTL model includes additive and dominance main effects of 1,619 markers and all pair-wise interactions, with a total of more than 5 million possible effects. This study not only reveals the major role of epistasis influencing rice yield, but also provides a set of candidate genetic loci for further experimental investigations. In the second application, we employ the EBlasso logistic regression model for pathway-based GWAS to include all possible main effects and a large number of pair-wise interactions of single nucleotide polymorphisms (SNPs) in a pathway, with a total number of more than 32 million effects included in the model. With effects inferred by EBlasso, the statistical significance of a pathway is tested with the Wald statistics and reliable effects in a significant pathway are selected using the stability selection technique. Another important area of genotype and phenotype association is to infer the structure of gene regulatory networks (GRNs). We developed a GRN inference algorithm by exploring sparse model selection and estimation methods in structural equation models (SEMs). We extend a previously developed sparse-aware maximum likelihood (SML) algorithm to incorporate the adaptive elastic net penalty for the SEM likelihood function (SEM-EN) and infer the model using a parallelized block coordinate ascent algorithm. With the versatile penalty function and powerful parallel computation, the SEM-EN algorithm is able to infer a network with thousands of nodes. The performance of the developed algorithm are demonstrated through simulation studies, in which power of detection and false discovery rate both suggest that SEM-EN significantly improves GRN inference over the previously developed SEM-SML algorithm. When applied to infer the GRN of a real budding yeast dataset with more than 3,000 nodes, SEM-EN infers a sparse network corroborated by previous independent studies in terms of roles of hub nodes and functions of key clusters. Given the fundamental importance of genotype and phenotype associations in understanding the genetic basis of complex biological system, the EBlasso-NE, EBlasso-NEG, EBEN, as well as SEM-EN algorithms and software packages developed in this dissertation achieve the effectiveness, robustness and efficiency that are needed for successful application to biology. With the advancement of high-throughput molecular technologies in generating information at genetic, epigenetic, transcriptional and post-transcriptional levels, the methods developed in this dissertation can have broad applications to infer different types of genotype and phenotypes associations.
Book Synopsis Gene Network Inference by : Alberto Fuente
Download or read book Gene Network Inference written by Alberto Fuente and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians.
Book Synopsis Sparse Modeling for Image and Vision Processing by : Julien Mairal
Download or read book Sparse Modeling for Image and Vision Processing written by Julien Mairal and published by Now Publishers. This book was released on 2014-12-19 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sparse Modeling for Image and Vision Processing offers a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.
Book Synopsis Statistical Analysis for High-Dimensional Data by : Arnoldo Frigessi
Download or read book Statistical Analysis for High-Dimensional Data written by Arnoldo Frigessi and published by Springer. This book was released on 2016-02-16 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.
Book Synopsis Introduction to Graphical Modelling by : David Edwards
Download or read book Introduction to Graphical Modelling written by David Edwards and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: A useful introduction to this topic for both students and researchers, with an emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, freely available for downloading from the Internet. Following a description of some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to this second edition and describe the use of directed, chain, and other graphs, complete with a summary of recent work on causal inference.
Book Synopsis Probabilistic Boolean Networks by : Ilya Shmulevich
Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.
Book Synopsis Protein-protein Interactions and Networks by : Anna Panchenko
Download or read book Protein-protein Interactions and Networks written by Anna Panchenko and published by Springer Science & Business Media. This book was released on 2010-04-06 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: The biological interactions of living organisms, and protein-protein interactions in particular, are astonishingly diverse. This comprehensive book provides a broad, thorough and multidisciplinary coverage of its field. It integrates different approaches from bioinformatics, biochemistry, computational analysis and systems biology to offer the reader a comprehensive global view of the diverse data on protein-protein interactions and protein interaction networks.
Download or read book Learning from Data written by Doug Fisher and published by Springer Science & Business Media. This book was released on 1996-05-02 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains a revised collection of papers originally presented at the Fifth International Workshop on Artificial Intelligence and Statistics in 1995. The topics represented in this volume are diverse, and include natural language application causality and graphical models, classification, learning, knowledge discovery, and exploratory data analysis. The chapters illustrate the rich possibilities for interdisciplinary study at the interface of artificial intelligence and statistics. The chapters vary in the background that they assume, but moderate familiarity with techniques of artificial intelligence and statistics is desirable in most cases.
Book Synopsis Cladag 2017 Book of Short Papers by : Francesca Greselin
Download or read book Cladag 2017 Book of Short Papers written by Francesca Greselin and published by Universitas Studiorum. This book was released on 2017-09-29 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the collection of the Abstract / Short Papers submitted by the authors of the International Conference of The CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), held in Milan (Italy) on September 13-15, 2017.
Book Synopsis Statistics for High-Dimensional Data by : Peter Bühlmann
Download or read book Statistics for High-Dimensional Data written by Peter Bühlmann and published by Springer Science & Business Media. This book was released on 2011-06-08 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
Book Synopsis Graphical Models, Exponential Families, and Variational Inference by : Martin J. Wainwright
Download or read book Graphical Models, Exponential Families, and Variational Inference written by Martin J. Wainwright and published by Now Publishers Inc. This book was released on 2008 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.
Book Synopsis Graph Representation Learning by : William L. William L. Hamilton
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Book Synopsis Science of Complex Networks by : J. F. F. Mendes
Download or read book Science of Complex Networks written by J. F. F. Mendes and published by American Institute of Physics. This book was released on 2005-07-12 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: All papers were peer-reviewed. The main goal of this conference is to combine the theories of statistical physics and random graph with the fundamental principles that govern the structure, function, and evolution of biological networks and modules. Applications to the Internet and WWW are also considered. In this proceedings, the reader will find an overview of the state-of-the-art of the new and fast growing field of complex networks.
Download or read book Biocomputing written by Panos M. Pardalos and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the quest to understand and model the healthy or sick human body, re searchers and medical doctors are utilizing more and more quantitative tools and techniques. This trend is pushing the envelope of a new field we call Biomedical Computing, as an exciting frontier among signal processing, pattern recognition, optimization, nonlinear dynamics, computer science and biology, chemistry and medicine. A conference on Biocomputing was held during February 25-27, 2001 at the University of Florida. The conference was sponsored by the Center for Applied Optimization, the Computational Neuroengineering Center, the Biomedical En gineering Program (through a Whitaker Foundation grant), the Brain Institute, the School of Engineering, and the University of Florida Research & Graduate Programs. The conference provided a forum for researchers to discuss and present new directions in Biocomputing. The well-attended three days event was highlighted by the presence of top researchers in the field who presented their work in Biocomputing. This volume contains a selective collection of ref ereed papers based on talks presented at this conference. You will find seminal contributions in genomics, global optimization, computational neuroscience, FMRI, brain dynamics, epileptic seizure prediction and cancer diagnostics. We would like to take the opportunity to thank the sponsors, the authors of the papers, the anonymous referees, and Kluwer Academic Publishers for making the conference successful and the publication of this volume possible. Panos M. Pardalos and Jose C.
