Applied Statistics for Network Biology

Applied Statistics for Network Biology
Author: Matthias Dehmer
Publisher: John Wiley & Sons
Total Pages: 441
Release: 2011-04-08
Genre: Medical
ISBN: 3527638083

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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.

Handbook of Statistical Bioinformatics

Handbook of Statistical Bioinformatics
Author: Henry Horng-Shing Lu
Publisher: Springer Nature
Total Pages: 406
Release: 2022-12-08
Genre: Science
ISBN: 3662659026

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Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

Computational Network Analysis with R

Computational Network Analysis with R
Author: Matthias Dehmer
Publisher: John Wiley & Sons
Total Pages: 368
Release: 2016-07-22
Genre: Medical
ISBN: 3527694404

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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.

Weighted Network Analysis

Weighted Network Analysis
Author: Steve Horvath
Publisher: Springer Science & Business Media
Total Pages: 433
Release: 2011-04-30
Genre: Science
ISBN: 144198819X

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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.

Proceedings of a Workshop on Statistics on Networks

Proceedings of a Workshop on Statistics on Networks
Author: Scott T. Weidman
Publisher: National Academies Press
Total Pages: 470
Release: 2007-10-30
Genre: Computers
ISBN: 0309101050

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A large number of biological, physical, and social systems contain complex networks. Knowledge about how these networks operate is critical for advancing a more general understanding of network behavior. To this end, each of these disciplines has created different kinds of statistical theory for inference on network data. To help stimulate further progress in the field of statistical inference on network data, the NRC sponsored a workshop that brought together researchers who are dealing with network data in different contexts. This book - which is available on CD only - contains the text of the 18 workshop presentations. The presentations focused on five major areas of research: network models, dynamic networks, data and measurement on networks, robustness and fragility of networks, and visualization and scalability of networks.

Analyzing Network Data in Biology and Medicine

Analyzing Network Data in Biology and Medicine
Author: Nataša Pržulj
Publisher: Cambridge University Press
Total Pages: 647
Release: 2019-03-28
Genre: Science
ISBN: 1108386245

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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

Discriminative Pattern Discovery on Biological Networks
Author: Fabio Fassetti
Publisher: Springer
Total Pages: 51
Release: 2017-09-01
Genre: Computers
ISBN: 3319634771

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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.

Applied Computational Biology and Statistics in Biotechnology and Bioinformatics

Applied Computational Biology and Statistics in Biotechnology and Bioinformatics
Author: Ajit Kumar Roy
Publisher: New India Publishing
Total Pages: 542
Release: 2012-01-15
Genre: Bioinformatics
ISBN: 9789380235929

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The book entitled "Applied Computational Biology and Statistics in Biotechnology and Bioinformatics" is aimed to cater to the growing demand of academia, researchers and commercial ventures. Altogether there are forty four chapters divided into the following broad sections like 1. Bioinformatics, Genomics and Proteomics, 2. Phylogeny 3. Drug Design and Epigenomics 4. Advanced Computational Tools and Techniques 5. Statistical methods for computational biology, data mining and visualization 6. Socio Economics and Ethics. This book presents the foundations of key problems in computational molecular biology and bioinformatics. It contains basic molecular biology concepts, tools, techniques and ways to measure sequence similarity, presents simple applications of searching sequence databases. After introducing methods for aligning multiple biological sequences and genomes, the text explores applications of the phylogenetic tree, methods for comparing phylogenetic trees, the problem of gene expression and motif finding. Interestingly, it is attempted to introduce computational biology without formulas that presents the biological and computational ideas in a relatively simple manner. It focuses on computational and statistical principles applied to genomes, and introduces the computational statistics that are crucial for understanding and visualization of problems. This makes the material accessible to Statistician and computer scientists without biological training, as well as to biologists with limited background in Statistics and computer science. Furthermore one chapter has been exclusively devoted to computational biology and computational statistics as applied in biotechnology illustrated with methodology, application and interpretation of results. More than four hundred figures, illustrations and diagrams reinforce concepts and present key results from the primary literature that will be very much useful to grasp on the subject, visualize the output and make right interpretation of the result. The book will be useful for all those working in Biotechnology sector in general and particularly researchers working in the laboratories of ICAR, CSIR, SAU's and many more institutions engaged R&D activities.

Networks of Networks in Biology

Networks of Networks in Biology
Author: Narsis A. Kiani
Publisher: Cambridge University Press
Total Pages: 215
Release: 2021-04
Genre: Computers
ISBN: 1108428878

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Introduces network inspired approaches for the analysis and integration of large, heterogeneous data sets in the life sciences.

Fundamentals Of Network Biology

Fundamentals Of Network Biology
Author: Wenjun Zhang
Publisher: World Scientific
Total Pages: 568
Release: 2018-05-18
Genre: Medical
ISBN: 1786345102

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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.