<p><p><b>Bayesian Networks in R with Applications in Systems Biology</b> 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
Computational Network Analysis with R: Applications in Biology, Medicine and Chemistry
β Scribed by Matthias Dehmer, Yongtang Shi, Frank Emmert-Streib (eds.)
- Publisher
- Wiley-VCH
- Year
- 2016
- Tongue
- English
- Leaves
- 355
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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.
β¦ Subjects
Biostatistics;Biology;Biological Sciences;Science & Math;General & Reference;Chemistry;Science & Math;Administration & Medicine Economics;Allied Health Professions;Basic Sciences;Dentistry;History;Medical Informatics;Medicine;Nursing;Pharmacology;Psychology;Research;Veterinary Medicine;Reference;Atlases;Dictionaries & Terminology;Drug Guides;Instruments & Supplies;Medicine & Health Sciences;New, Used & Rental Textbooks;Specialty Boutique;General;Medicine;Medicine & Health Sciences;New, Used & Re
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Introduction -- Bayesian Networks in the Absence of Temporal Information -- Bayesian Networds in the Presence of Temporal Information -- Bayesian Network Inference Algorithms -- Parallel Computing for Bayesian Networks -- Solutions -- Index -- References.;Bayesian Networks in R with Applications in
<p><p><b>Bayesian Networks in R with Applications in Systems Biology</b> 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
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