Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

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Publisher : OUP Oxford
ISBN 13 : 0191019194
Total Pages : 483 pages
Book Rating : 4.1/5 (91 download)

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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.

Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

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Publisher :
ISBN 13 : 9780191779619
Total Pages : 449 pages
Book Rating : 4.7/5 (796 download)

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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 . This book was released on 2014 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Big Data Analytics in Genomics

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Publisher : Springer
ISBN 13 : 3319412795
Total Pages : 426 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Big Data Analytics in Genomics by : Ka-Chun Wong

Download or read book Big Data Analytics in Genomics written by Ka-Chun Wong and published by Springer. This book was released on 2016-10-24 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.

Probabilistic Graphical Models

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Publisher : Springer
ISBN 13 : 3319114336
Total Pages : 609 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Probabilistic Graphical Models by : Linda C. van der Gaag

Download or read book Probabilistic Graphical Models written by Linda C. van der Gaag and published by Springer. This book was released on 2014-09-11 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The 38 revised full papers presented in this book were carefully reviewed and selected from 44 submissions. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning.

Probabilistic Graphical Models

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Publisher : MIT Press
ISBN 13 : 0262258358
Total Pages : 1270 pages
Book Rating : 4.2/5 (622 download)

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Book Synopsis Probabilistic Graphical Models by : Daphne Koller

Download or read book Probabilistic Graphical Models written by Daphne Koller and published by MIT Press. This book was released on 2009-07-31 with total page 1270 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Plant Systems Biology

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Publisher : Springer Science & Business Media
ISBN 13 : 376437439X
Total Pages : 362 pages
Book Rating : 4.7/5 (643 download)

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Book Synopsis Plant Systems Biology by : Sacha Baginsky

Download or read book Plant Systems Biology written by Sacha Baginsky and published by Springer Science & Business Media. This book was released on 2007-06-25 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume aims to provide a timely view of the state-of-the-art in systems biology. The editors take the opportunity to define systems biology as they and the contributing authors see it, and this will lay the groundwork for future studies. The volume is well-suited to both students and researchers interested in the methods of systems biology. Although the focus is on plant systems biology, the proposed material could be suitably applied to any organism.

Bayesian Networks in R

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Publisher : Springer Science & Business Media
ISBN 13 : 1461464463
Total Pages : 168 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Bayesian Networks in R by : Radhakrishnan Nagarajan

Download or read book Bayesian Networks in R written by Radhakrishnan Nagarajan and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.

Graphical Heritage

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Publisher : Springer Nature
ISBN 13 : 303047979X
Total Pages : 791 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Graphical Heritage by : Luis Agustín-Hernández

Download or read book Graphical Heritage written by Luis Agustín-Hernández and published by Springer Nature. This book was released on 2020-05-11 with total page 791 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 18th International Conference on Graphic Design in Architecture, EGA 2020, focusing on heritage – including architectural and graphic heritage as well as the graphics of heritage. This first volume gathers selected contributions covering theories, and new technologies and findings to help shed light on current questions related to heritage. It features original documentation studies on historical archives, 3D and solid representation of architectural objects, as well as virtual graphic representation and applications of augmented reality, all documenting and/or reconstructing the present, past and future of architectural objects. As such, this book offers extensive and timely information to architectural and graphic designers, urban designers and engineers, and industrial designers and historians.

Bayesian Reasoning and Machine Learning

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Publisher : Cambridge University Press
ISBN 13 : 0521518148
Total Pages : 739 pages
Book Rating : 4.5/5 (215 download)

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Book Synopsis Bayesian Reasoning and Machine Learning by : David Barber

Download or read book Bayesian Reasoning and Machine Learning written by David Barber and published by Cambridge University Press. This book was released on 2012-02-02 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Weighted Network Analysis

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Publisher : Springer Science & Business Media
ISBN 13 : 144198819X
Total Pages : 433 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Weighted Network Analysis by : Steve Horvath

Download or read book Weighted Network Analysis written by Steve Horvath and published by Springer Science & Business Media. This book was released on 2011-04-30 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Anticancer Plants: Mechanisms and Molecular Interactions

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Publisher : Springer
ISBN 13 : 9811084173
Total Pages : 365 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Anticancer Plants: Mechanisms and Molecular Interactions by : Mohd Sayeed Akhtar

Download or read book Anticancer Plants: Mechanisms and Molecular Interactions written by Mohd Sayeed Akhtar and published by Springer. This book was released on 2018-07-03 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the application of plant derived anticancer compounds as chemopreventives to treat several cancer types, focusing on the molecular mechanisms of action of phytocompounds and providing an overview of the basic processes at the cellular and molecular level that are involved in the progression of the cancer and can be employed in targeted preventive therapies. In addition, it highlights the development of novel anticancer drugs from plant sources using bioinformatics approaches. The compiled chapter data aids readers understanding of issues related to bioavailability, toxic effects and mechanisms of action of phytocompounds, and helps them identify the leads and utilize them against various cancer types effectively. Furthermore, it promotes the use of bioinformatics tools in medicinal plants to expedite their use in plant breeding programs to develop molecular markers to distinguish disease subtypes and predicting mutation, which in turn improves cancer diagnosis and prognosis, and to develop new lead compounds computationally. The book provides scientific verifications of plant compounds mechanisms of action against various cancers and offers useful information for students, teachers, and healthcare professionals involved in drug discovery, and clinical and therapeutic research.

Systems Biology

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Publisher : Cambridge University Press
ISBN 13 : 1107038855
Total Pages : 551 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Systems Biology by : Bernhard Palsson

Download or read book Systems Biology written by Bernhard Palsson and published by Cambridge University Press. This book was released on 2015-01-26 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive single-authored textbook on genome-scale models and the bottom-up approach to systems biology.

Genomes 3

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Publisher : Garland Science
ISBN 13 : 0815341385
Total Pages : 736 pages
Book Rating : 4.8/5 (153 download)

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Book Synopsis Genomes 3 by : Terence A. Brown

Download or read book Genomes 3 written by Terence A. Brown and published by Garland Science. This book was released on 2007 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: The VitalBook e-book version of Genomes 3 is only available in the US and Canada at the present time. To purchase or rent please visit http://store.vitalsource.com/show/9780815341383 Covering molecular genetics from the basics through to genome expression and molecular phylogenetics, Genomes 3is the latest edition of this pioneering textbook. Updated to incorporate the recent major advances, Genomes 3 is an invaluable companion for any undergraduate throughout their studies in molecular genetics. Genomes 3 builds on the achievements of the previous two editions by putting genomes, rather than genes, at the centre of molecular genetics teaching. Recognizing that molecular biology research was being driven more by genome sequencing and functional analysis than by research into genes, this approach has gathered momentum in recent years.

Gene Network Inference

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Publisher : Springer Science & Business Media
ISBN 13 : 3642451616
Total Pages : 135 pages
Book Rating : 4.6/5 (424 download)

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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.

Mathematical Biology

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Publisher : Springer Science & Business Media
ISBN 13 : 0387709843
Total Pages : 552 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Mathematical Biology by : Ronald W. Shonkwiler

Download or read book Mathematical Biology written by Ronald W. Shonkwiler and published by Springer Science & Business Media. This book was released on 2009-08-04 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents mathematical biology as a field with a unity of its own, rather than only the intrusion of one science into another. The book focuses on problems of contemporary interest, such as cancer, genetics, and the rapidly growing field of genomics.

Comparative and Evolutionary Genomics of Angiosperm Trees

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Publisher : Springer
ISBN 13 : 3319493299
Total Pages : 379 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Comparative and Evolutionary Genomics of Angiosperm Trees by : Andrew Groover

Download or read book Comparative and Evolutionary Genomics of Angiosperm Trees written by Andrew Groover and published by Springer. This book was released on 2017-11-21 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Marking the change in focus of tree genomics from single species to comparative approaches, this book covers biological, genomic, and evolutionary aspects of angiosperm trees that provide information and perspectives to support researchers broadening the focus of their research. The diversity of angiosperm trees in morphology, anatomy, physiology and biochemistry has been described and cataloged by various scientific disciplines, but the molecular, genetic, and evolutionary mechanisms underlying this diversity have only recently been explored. Excitingly, advances in genomic and sequencing technologies are ushering a new era of research broadly termed comparative genomics, which simultaneously exploits and describes the evolutionary origins and genetic regulation of traits of interest. Within tree genomics, this research is already underway, as the number of complete genome sequences available for angiosperm trees is increasing at an impressive pace and the number of species for which RNAseq data are available is rapidly expanding. Because they are extensively covered by other literature and are rapidly changing, technical and computational approaches—such as the latest sequencing technologies—are not a main focus of this book. Instead, this comprehensive volume provides a valuable, broader view of tree genomics whose relevance will outlive the particulars of current-day technical approaches. The first section of the book discusses background on the evolution and diversification of angiosperm trees, as well as offers description of the salient features and diversity of the unique physiology and wood anatomy of angiosperm trees. The second section explores the two most advanced model angiosperm tree species (poplars and eucalypts) as well as species that are soon to emerge as new models. The third section describes the structural features and evolutionary histories of angiosperm tree genomes, followed by a fourth section focusing on the genomics of traits of biological, ecological, and economic interest. In summary, this book is a timely and well-referenced foundational resource for the forest tree community looking to embrace comparative approaches for the study of angiosperm trees.

Learning in Graphical Models

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Publisher : Springer Science & Business Media
ISBN 13 : 9401150141
Total Pages : 658 pages
Book Rating : 4.4/5 (11 download)

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Book Synopsis Learning in Graphical Models by : M.I. Jordan

Download or read book Learning in Graphical Models written by M.I. Jordan and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.