Probabilistic Graphical Models And Algorithms For Genomic Analysis
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Probabilistic Graphical Models And Algorithms For Genomic Analysis
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Author : Poe Xing
language : en
Publisher:
Release Date : 2004
Probabilistic Graphical Models And Algorithms For Genomic Analysis written by Poe Xing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Probabilistic Graphical Models For Genetics Genomics And Postgenomics
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Author : Raphaël Mourad
language : en
Publisher: OUP Oxford
Release Date : 2014-09-18
Probabilistic Graphical Models For Genetics Genomics And Postgenomics written by Raphaël Mourad and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-18 with Science categories.
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.
Computational Analysis Of Gene Family Evolution By Probabilistic Graphical Models
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Author : Chi Quynh Nguyen
language : en
Publisher:
Release Date : 2004
Computational Analysis Of Gene Family Evolution By Probabilistic Graphical Models written by Chi Quynh Nguyen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Probabilistic Graphical Models For Genetics Genomics And Postgenomics
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Author : Christine Sinoquet
language : en
Publisher:
Release Date : 2014
Probabilistic Graphical Models For Genetics Genomics And Postgenomics written by Christine Sinoquet and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Genetics categories.
At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest.
Evaluating The Reproducibility Of Segmentation And Genome Annotation Saga Algorithms
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Author : Mehdi Foroozandeh Shahraki
language : en
Publisher:
Release Date : 2022
Evaluating The Reproducibility Of Segmentation And Genome Annotation Saga Algorithms written by Mehdi Foroozandeh Shahraki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Segmentation and genome annotation (SAGA) algorithms such as ChromHMM and Segway are widely used for genome annotation using epigenomic datasets. These algorithms rely on probabilistic graphical models and take as input a collection of genomics datasets, partition the genome, and assign a label to each segment such that positions with the same label have similar patterns in the input data and output an annotation that assigns to each genomic position its annotated activity, such as Enhancer, Transcribed, etc. Despite the widespread applications of SAGA methods, there is currently no principled way to evaluate the statistical significance of SAGA label assignments. In this study, we are applying principles of reproducibility analysis to assess the statistical significance and the confidence that is to be ascribed to the genome annotations obtained from SAGA algorithms. Moreover, by investigating various individual variables that affect reproducibility, we try to delineate different sources of irreproducibility in genome annotations. We hypothesize that reproducibility measurements provide more realistic confidence estimates of the SAGA annotations, which will uncover irreproducible elements in existing annotations and remove doubt in those that stand up to this statistical scrutiny.
Comparative Gene Finding
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Author : Marina Axelson-Fisk
language : en
Publisher: Springer
Release Date : 2015-04-13
Comparative Gene Finding written by Marina Axelson-Fisk and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-13 with Computers categories.
This book presents a guide to building computational gene finders, and describes the state of the art in computational gene finding methods, with a focus on comparative approaches. Fully updated and expanded, this new edition examines next-generation sequencing (NGS) technology. The book also discusses conditional random fields, enhancing the broad coverage of topics spanning probability theory, statistics, information theory, optimization theory and numerical analysis. Features: introduces the fundamental terms and concepts in the field; discusses algorithms for single-species gene finding, and approaches to pairwise and multiple sequence alignments, then describes how the strengths in both areas can be combined to improve the accuracy of gene finding; explores the gene features most commonly captured by a computational gene model, and explains the basics of parameter training; illustrates how to implement a comparative gene finder; examines NGS techniques and how to build a genome annotation pipeline.
Probabilistic Graphical Models For Genetics Genomics And Postgenomics
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Author : Christine Sinoquet
language : en
Publisher: Oxford University Press, USA
Release Date : 2014
Probabilistic Graphical Models For Genetics Genomics And Postgenomics written by Christine Sinoquet and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Mathematics categories.
At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play.
Bioinformatics Algorithms
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Author : Ion Mandoiu
language : en
Publisher: John Wiley & Sons
Release Date : 2008-02-25
Bioinformatics Algorithms written by Ion Mandoiu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-25 with Computers categories.
Presents algorithmic techniques for solving problems in bioinformatics, including applications that shed new light on molecular biology This book introduces algorithmic techniques in bioinformatics, emphasizing their application to solving novel problems in post-genomic molecular biology. Beginning with a thought-provoking discussion on the role of algorithms in twenty-first-century bioinformatics education, Bioinformatics Algorithms covers: General algorithmic techniques, including dynamic programming, graph-theoretical methods, hidden Markov models, the fast Fourier transform, seeding, and approximation algorithms Algorithms and tools for genome and sequence analysis, including formal and approximate models for gene clusters, advanced algorithms for non-overlapping local alignments and genome tilings, multiplex PCR primer set selection, and sequence/network motif finding Microarray design and analysis, including algorithms for microarray physical design, missing value imputation, and meta-analysis of gene expression data Algorithmic issues arising in the analysis of genetic variation across human population, including computational inference of haplotypes from genotype data and disease association search in case/control epidemiologic studies Algorithmic approaches in structural and systems biology, including topological and structural classification in biochemistry, and prediction of protein-protein and domain-domain interactions Each chapter begins with a self-contained introduction to a computational problem; continues with a brief review of the existing literature on the subject and an in-depth description of recent algorithmic and methodological developments; and concludes with a brief experimental study and a discussion of open research challenges. This clear and approachable presentation makes the book appropriate for researchers, practitioners, and graduate students alike.
Data Analysis And Visualization In Genomics And Proteomics
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Author : Francisco Azuaje
language : en
Publisher: John Wiley & Sons
Release Date : 2005-06-24
Data Analysis And Visualization In Genomics And Proteomics written by Francisco Azuaje and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-24 with Science categories.
Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems. One of the first systematic overviews of the problem of biological data integration using computational approaches This book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scale Places emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems
Sparse Model Building From Genome Wide Variation With Graphical Models
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Author : Benjamin Alexander Logsdon
language : en
Publisher:
Release Date : 2011
Sparse Model Building From Genome Wide Variation With Graphical Models written by Benjamin Alexander Logsdon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
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.