Applied Statistics For Network Biology

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Applied Statistics for Network Biology

Author : Matthias Dehmer
Publisher : John Wiley & Sons
ISBN 13 : 3527638083
Total Pages : 441 pages
Book Rating : 4.5/5 (276 download)

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Book Synopsis Applied Statistics for Network Biology by : Matthias Dehmer

Download or read book Applied Statistics for Network Biology written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2011-04-08 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.

Fundamentals Of Network Biology

Author : Wenjun Zhang
Publisher : World Scientific
ISBN 13 : 1786345102
Total Pages : 568 pages
Book Rating : 4.7/5 (863 download)

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Book Synopsis Fundamentals Of Network Biology by : Wenjun Zhang

Download or read book Fundamentals Of Network Biology written by Wenjun Zhang and published by World Scientific. This book was released on 2018-05-18 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the first comprehensive title on network biology, this book covers a wide range of subjects including scientific fundamentals (graphs, networks, etc) of network biology, construction and analysis of biological networks, methods for identifying crucial nodes in biological networks, link prediction, flow analysis, network dynamics, evolution, simulation and control, ecological networks, social networks, molecular and cellular networks, network pharmacology and network toxicology, big data analytics, and more.Across 12 parts and 26 chapters, with Matlab codes provided for most models and algorithms, this self-contained title provides an in-depth and complete insight on network biology. It is a valuable read for high-level undergraduates and postgraduates in the areas of biology, ecology, environmental sciences, medical science, computational science, applied mathematics, and social science.

Analyzing Network Data in Biology and Medicine

Author : Nataša Pržulj
Publisher : Cambridge University Press
ISBN 13 : 1108386245
Total Pages : 647 pages
Book Rating : 4.1/5 (83 download)

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Book Synopsis Analyzing Network Data in Biology and Medicine by : Nataša Pržulj

Download or read book Analyzing Network Data in Biology and Medicine written by Nataša Pržulj and published by Cambridge University Press. This book was released on 2019-03-28 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network data analysis in biomedicine.

Discriminative Pattern Discovery on Biological Networks

Author : Fabio Fassetti
Publisher : Springer
ISBN 13 : 3319634771
Total Pages : 51 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Discriminative Pattern Discovery on Biological Networks by : Fabio Fassetti

Download or read book Discriminative Pattern Discovery on Biological Networks written by Fabio Fassetti and published by Springer. This book was released on 2017-09-01 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes). Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples). In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population.

Networks of Networks in Biology

Author : Narsis A. Kiani
Publisher : Cambridge University Press
ISBN 13 : 1108428878
Total Pages : 215 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Networks of Networks in Biology by : Narsis A. Kiani

Download or read book Networks of Networks in Biology written by Narsis A. Kiani and published by Cambridge University Press. This book was released on 2021-04 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces network inspired approaches for the analysis and integration of large, heterogeneous data sets in the life sciences.

Computational Network Analysis with R

Author : Matthias Dehmer
Publisher : John Wiley & Sons
ISBN 13 : 3527694404
Total Pages : 368 pages
Book Rating : 4.5/5 (276 download)

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Book Synopsis Computational Network Analysis with R by : Matthias Dehmer

Download or read book Computational Network Analysis with R written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2016-07-22 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

Computational Network Theory

Author : Matthias Dehmer
Publisher : John Wiley & Sons
ISBN 13 : 3527691537
Total Pages : 200 pages
Book Rating : 4.5/5 (276 download)

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Book Synopsis Computational Network Theory by : Matthias Dehmer

Download or read book Computational Network Theory written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2015-04-28 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data. The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.

Advances in Network Complexity

Author : Matthias Dehmer
Publisher : John Wiley & Sons
ISBN 13 : 3527670483
Total Pages : 298 pages
Book Rating : 4.5/5 (276 download)

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Book Synopsis Advances in Network Complexity by : Matthias Dehmer

Download or read book Advances in Network Complexity written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2013-06-21 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: A well-balanced overview of mathematical approaches to complex systems ranging from applications in chemistry and ecology to basic research questions on network complexity. Matthias Dehmer, Abbe Mowshowitz, and Frank Emmert-Streib, well-known pioneers in the fi eld, have edited this volume with a view to balancing classical and modern approaches to ensure broad coverage of contemporary research problems. The book is a valuable addition to the literature and a must-have for anyone dealing with network compleaity and complexity issues.

Statistical Diagnostics for Cancer

Author : Matthias Dehmer
Publisher : John Wiley & Sons
ISBN 13 : 3527665455
Total Pages : 301 pages
Book Rating : 4.5/5 (276 download)

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Book Synopsis Statistical Diagnostics for Cancer by : Matthias Dehmer

Download or read book Statistical Diagnostics for Cancer written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-11-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.

Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Author : Christine Sinoquet
Publisher : Oxford University Press, USA
ISBN 13 : 0198709021
Total Pages : 483 pages
Book Rating : 4.1/5 (987 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 Oxford University Press, USA. This book was released on 2014 with total page 483 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.

Data Integration in the Life Sciences

Author : Christopher J.O. Baker
Publisher : Springer
ISBN 13 : 364239437X
Total Pages : 151 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Data Integration in the Life Sciences by : Christopher J.O. Baker

Download or read book Data Integration in the Life Sciences written by Christopher J.O. Baker and published by Springer. This book was released on 2013-06-22 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th International Conference on Data Integration in the Life Sciences, DILS 2013, held in Montreal, QC, Canada, in July 2013. The 10 revised papers included in this volume were carefully reviewed and selected from 23 submissions. The papers cover a range of important topics such as algorithms for ontology matching, interoperable frameworks for text mining using semantic web services, pipelines for genome-wide functional annotation, automation of pipelines providing data discovery and access to distributed resources, knowledge-driven querying-answer systems, prizms, nanopublications, electronic health records and linked data.

Statistical Modelling of Molecular Descriptors in QSAR/QSPR

Author : Matthias Dehmer
Publisher : John Wiley & Sons
ISBN 13 : 3527645012
Total Pages : 437 pages
Book Rating : 4.5/5 (276 download)

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Book Synopsis Statistical Modelling of Molecular Descriptors in QSAR/QSPR by : Matthias Dehmer

Download or read book Statistical Modelling of Molecular Descriptors in QSAR/QSPR written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-09-13 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR. The high-profile international author and editor team ensures excellent coverage of the topic, making this a must-have for everyone working in chemoinformatics and structure-oriented drug design.

Transcriptomics in Health and Disease

Author : Geraldo A. Passos
Publisher : Springer Nature
ISBN 13 : 303087821X
Total Pages : 473 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Transcriptomics in Health and Disease by : Geraldo A. Passos

Download or read book Transcriptomics in Health and Disease written by Geraldo A. Passos and published by Springer Nature. This book was released on 2022-03-07 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of transcriptomics is key to understanding complex diseases. This new edition will build on the foundation of the first edition while incorporating the progress that has been made in the field of transcriptomics in the past six years, including bioinformatics for data analysis. Written by leading experts, chapters address new subjects such as methodological advances in large-scale sequencing, the sequencing of single-cells, and spatial transcriptomics. The new edition will address how transcriptomics may be used in combination with genetic strategies to identify causative genes in monogenic and complex genetic diseases. Coverage will also explore transcriptomics in challenging groups of diseases, such as cancer, inflammation, bacterial infection, and autoimmune diseases. The updated volume will be useful for geneticists, genome biologists, biomedical researchers, molecular biologists, bioinformaticians, and students, among others.

Statistical Analysis of Network Data with R

Author : Eric D. Kolaczyk
Publisher : Springer
ISBN 13 : 1493909835
Total Pages : 214 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Statistical Analysis of Network Data with R by : Eric D. Kolaczyk

Download or read book Statistical Analysis of Network Data with R written by Eric D. Kolaczyk and published by Springer. This book was released on 2014-05-22 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

Essentials of Bioinformatics, Volume I

Author : Noor Ahmad Shaik
Publisher : Springer
ISBN 13 : 3030026345
Total Pages : 410 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Essentials of Bioinformatics, Volume I by : Noor Ahmad Shaik

Download or read book Essentials of Bioinformatics, Volume I written by Noor Ahmad Shaik and published by Springer. This book was released on 2019-03-27 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bioinformatics is an integrative field of computer science, genetics, genomics, proteomics, and statistics, which has undoubtedly revolutionized the study of biology and medicine in past decades. It mainly assists in modeling, predicting and interpreting large multidimensional biological data by utilizing advanced computational methods. Despite its enormous potential, bioinformatics is not widely integrated into the academic curriculum as most life science students and researchers are still not equipped with the necessary knowledge to take advantage of this powerful tool. Hence, the primary purpose of our book is to supplement this unmet need by providing an easily accessible platform for students and researchers starting their career in life sciences. This book aims to avoid sophisticated computational algorithms and programming. Instead, it mostly focuses on simple DIY analysis and interpretation of biological data with personal computers. Our belief is that once the beginners acquire these basic skillsets, they will be able to handle most of the bioinformatics tools for their research work and to better understand their experimental outcomes. Unlike other bioinformatics books which are mostly theoretical, this book provides practical examples for the readers on state-of-the-art open source tools to solve biological problems. Flow charts of experiments, graphical illustrations, and mock data are included for quick reference. Volume I is therefore an ideal companion for students and early stage professionals wishing to master this blooming field.

Encyclopedia of Bioinformatics and Computational Biology

Author :
Publisher : Elsevier
ISBN 13 : 0128114320
Total Pages : 3421 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Encyclopedia of Bioinformatics and Computational Biology by :

Download or read book Encyclopedia of Bioinformatics and Computational Biology written by and published by Elsevier. This book was released on 2018-08-21 with total page 3421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases

A Survey of Statistical Network Models

Author : Anna Goldenberg
Publisher : Now Publishers Inc
ISBN 13 : 1601983204
Total Pages : 118 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis A Survey of Statistical Network Models by : Anna Goldenberg

Download or read book A Survey of Statistical Network Models written by Anna Goldenberg and published by Now Publishers Inc. This book was released on 2010 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.