Gene Network Inference

Gene Network Inference

Author: Alberto Fuente

Publisher: Springer Science & Business Media

Published: 2014-01-03

Total Pages: 135

ISBN-13: 3642451616

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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.


Gene Regulatory Networks

Gene Regulatory Networks

Author: Guido Sanguinetti

Publisher: Humana

Published: 2018-12-14

Total Pages: 0

ISBN-13: 9781493988815

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This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the extraction of causal information from biological data; GRN inference from multiple heterogeneous data sets; non-parametric and hybrid statistical methods; the joint inference of differential networks; and mechanistic models of gene regulation dynamics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, descriptions of recently developed methods for GRN inference, applications of these methods on real and/ or simulated biological data, and step-by-step tutorials on the usage of associated software tools. Cutting-edge and thorough, Gene Regulatory Networks: Methods and Protocols is an essential tool for evaluating the current research needed to further address the common challenges faced by specialists in this field.


Probabilistic Boolean Networks

Probabilistic Boolean Networks

Author: Ilya Shmulevich

Publisher: SIAM

Published: 2010-01-21

Total Pages: 276

ISBN-13: 0898716926

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The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.


Weighted Network Analysis

Weighted Network Analysis

Author: Steve Horvath

Publisher: Springer Science & Business Media

Published: 2011-04-30

Total Pages: 433

ISBN-13: 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.


Plant Gene Regulatory Networks

Plant Gene Regulatory Networks

Author: Kerstin Kaufmann

Publisher: Humana

Published: 2017-06-17

Total Pages: 0

ISBN-13: 9781493971244

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This volume presents protocols that analyze and explore gene regulatory networks (GRNs) at different levels in plants. This book is divided into two parts: Part I introduces different experimental techniques used to study genes and their regulatory interactions in plants. Part II highlights different computational approaches used for the integration of experimental data and bioinformatics-based predictions of regulatory interactions. This part of the book also provides information on essential database resources that grant access to gene-regulatory and molecular interactions in different plant genomes, with a specific focus on Arabidopsis thaliana. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Thorough and cutting-edge, Plant Gene Regulatory Networks: Methods and Protocols is a valuable resource for scientists and researchers interested in expanding their knowledge of GRNs.


Drosophila Eye Development

Drosophila Eye Development

Author: Kevin Moses

Publisher: Springer Science & Business Media

Published: 2002-03-12

Total Pages: 296

ISBN-13: 9783540425908

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1 Kevin Moses It is now 25 years since the study of the development of the compound eye in Drosophila really began with a classic paper (Ready et al. 1976). In 1864, August Weismann published a monograph on the development of Diptera and included some beautiful drawings of the developing imaginal discs (Weismann 1864). One of these is the first description of the third instar eye disc in which Weismann drew a vertical line separating a posterior domain that included a regular pattern of clustered cells from an anterior domain without such a pattern. Weismann suggested that these clusters were the precursors of the adult ommatidia and that the line marks the anterior edge of the eye. In his first suggestion he was absolutely correct - in his second he was wrong. The vertical line shown was not the anterior edge of the eye, but the anterior edge of a moving wave of patterning and cell type specification that 112 years later (1976) Ready, Hansen and Benzer would name the "morphogenetic furrow". While it is too late to hear from August Weismann, it is a particular pleasure to be able to include a chapter in this Volume from the first author of that 1976 paper: Don Ready! These past 25 years have seen an astonishing explosion in the study of the fly eye (see Fig.


Computational Modeling of Gene Regulatory Networks

Computational Modeling of Gene Regulatory Networks

Author: Hamid Bolouri

Publisher: Imperial College Press

Published: 2008

Total Pages: 341

ISBN-13: 1848162200

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This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology.


Network Inference in Molecular Biology

Network Inference in Molecular Biology

Author: Jesse M. Lingeman

Publisher: Springer Science & Business Media

Published: 2012-05-24

Total Pages: 106

ISBN-13: 1461431131

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Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation. Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable.


Computational Intelligence and Bioinformatics

Computational Intelligence and Bioinformatics

Author: De-Shuang Huang

Publisher: Springer

Published: 2006-09-01

Total Pages: 824

ISBN-13: 3540372822

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This book constitutes the refereed proceedings of the International Conference on Intelligent Computing, ICIC 2006, held in Kunming, China, in August 2006. The book presents 165 revised full papers, carefully reviewed. Topics covered include ant colony optimization, particle swarm optimization, swarm intelligence, autonomy-oriented computing, quantum and molecular computations, biological and DNA computing, intelligent computing in bioinformatics, intelligent computing in computational biology and drug design, computational genomics and proteomics, and more.