Applied Statistics For Network Biology

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Applied Statistics For Network Biology

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Author : Matthias Dehmer
language : en
Publisher: John Wiley & Sons
Release Date : 2011-04-08

Applied Statistics For Network Biology written by Matthias Dehmer 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 2011-04-08 with Medical categories.

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.

Applied Statistics In Agricultural Biological And Environmental Sciences

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Author : Barry Glaz
language : en
Publisher: John Wiley & Sons
Release Date : 2020-01-22

Applied Statistics In Agricultural Biological And Environmental Sciences written by Barry Glaz 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 2020-01-22 with Technology & Engineering categories.

Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors cover fundamental concepts and methodologies of experimental design and analysis, and also delve into advanced statistical topics, all explored by analyzing real agronomic data with practical and creative approaches using available software tools. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed.

Discriminative Pattern Discovery On Biological Networks

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Author : Fabio Fassetti
language : en
Publisher: Springer
Release Date : 2017-09-01

Discriminative Pattern Discovery On Biological Networks written by Fabio Fassetti and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-01 with Computers categories.

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.

Computational Network Analysis With R

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Author : Matthias Dehmer
language : en
Publisher: John Wiley & Sons
Release Date : 2016-08-09

Computational Network Analysis With R written by Matthias Dehmer 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 2016-08-09 with Medical categories.

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.

Statistical Diagnostics For Cancer

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Author : Matthias Dehmer
language : en
Publisher: John Wiley & Sons
Release Date : 2012-11-28

Statistical Diagnostics For Cancer written by Matthias Dehmer 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 2012-11-28 with Medical categories.

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

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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.

Data Integration In The Life Sciences

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Author : Christopher J.O. Baker
language : en
Publisher: Springer
Release Date : 2013-06-22

Data Integration In The Life Sciences written by Christopher J.O. Baker and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-22 with Computers categories.

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

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Author : Matthias Dehmer
language : en
Publisher: John Wiley & Sons
Release Date : 2012-09-13

Statistical Modelling Of Molecular Descriptors In Qsar Qspr written by Matthias Dehmer 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 2012-09-13 with Medical categories.

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.

Encyclopedia Of Bioinformatics And Computational Biology

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Author :
language : en
Publisher: Elsevier
Release Date : 2018-08-21

Encyclopedia Of Bioinformatics And Computational Biology written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-21 with Medical categories.

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

Essentials Of Bioinformatics Volume I

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Author : Noor Ahmad Shaik
language : en
Publisher: Springer
Release Date : 2019-03-27

Essentials Of Bioinformatics Volume I written by Noor Ahmad Shaik and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-27 with Computers categories.

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.