2010 Ieee International Workshop On Genomic Signal Processing And Statistics PDF Download
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Author | : IEEE Staff |
Publisher | : |
Total Pages | : |
Release | : 2011-02 |
Genre | : |
ISBN | : 9781612847917 |
Download 2010 IEEE International Workshop on Genomic Signal Processing and Statistics Book in PDF, ePub and Kindle
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Release | : 2010 |
Genre | : Genomics |
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Download 2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Book in PDF, ePub and Kindle
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Total Pages | : 37 |
Release | : 2010 |
Genre | : Genomics |
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Download GENSIPS Book in PDF, ePub and Kindle
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Total Pages | : |
Release | : 2009 |
Genre | : Genomics |
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Download Genomic Signal Processing and Statistics, 2009, GENSIPS 2009, IEEE International Workshop on Book in PDF, ePub and Kindle
Author | : Institute of Electrical and Electronics Engineers |
Publisher | : |
Total Pages | : |
Release | : 2006 |
Genre | : |
ISBN | : 9781424403851 |
Download Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on Book in PDF, ePub and Kindle
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Release | : 2007 |
Genre | : Genomics |
ISBN | : 9781424409990 |
Download GENSIPS '07 Book in PDF, ePub and Kindle
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Total Pages | : 105 |
Release | : 2013 |
Genre | : Bioinformatics |
ISBN | : 9781479934621 |
Download 2013 IEEE International Workshop on Genomic Signal Processing and Statistics Book in PDF, ePub and Kindle
Author | : Matthias Dehmer |
Publisher | : John Wiley & Sons |
Total Pages | : 301 |
Release | : 2012-11-28 |
Genre | : Medical |
ISBN | : 3527665455 |
Download Statistical Diagnostics for Cancer Book in PDF, ePub and Kindle
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.
Author | : Pey-Chang Kent Lin |
Publisher | : Springer Science & Business Media |
Total Pages | : 112 |
Release | : 2013-10-31 |
Genre | : Technology & Engineering |
ISBN | : 146149429X |
Download Logic Synthesis for Genetic Diseases Book in PDF, ePub and Kindle
This book brings to bear a body of logic synthesis techniques, in order to contribute to the analysis and control of Boolean Networks (BN) for modeling genetic diseases such as cancer. The authors provide several VLSI logic techniques to model the genetic disease behavior as a BN, with powerful implicit enumeration techniques. Coverage also includes techniques from VLSI testing to control a faulty BN, transforming its behavior to a healthy BN, potentially aiding in efforts to find the best candidates for treatment of genetic diseases.
Author | : Benjamin Haibe-Kains |
Publisher | : Frontiers Media SA |
Total Pages | : 192 |
Release | : 2015-04-14 |
Genre | : Bioengineering |
ISBN | : 2889194787 |
Download Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics Book in PDF, ePub and Kindle
Scientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput “omics” technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the major challenges of the post-genomic era is to extract functional information by integrating such heterogeneous high-throughput genomic data. This is not a trivial task as we are increasingly coming to understand that it is not individual genes, but rather biological pathways and networks that drive an organism’s response to environmental factors and the development of its particular phenotype. In order to fully understand the way in which these networks interact (or fail to do so) in specific states (disease for instance), we must learn both, the structure of the underlying networks and the rules that govern their behavior. In recent years there has been an increasing interest in methods that aim to infer biological networks. These methods enable the opportunity for better understanding the interactions between genomic features and the overall structure and behavior of the underlying networks. So far, such network models have been mainly used to identify and validate new interactions between genes of interest. But ultimately, one could use these networks to predict large-scale effects of perturbations, such as treatment by multiple targeted drugs. However, currently, we are still at an early stage of comprehending methods and approaches providing a robust statistical framework to quantitatively assess the quality of network inference and its predictive potential. The scope of this Research Topic in Bioinformatics and Computational Biology aims at addressing these issues by investigating the various, complementary approaches to quantify the quality of network models. These “validation” techniques could focus on assessing quality of specific interactions, global and local structures, and predictive ability of network models. These methods could rely exclusively on in silico evaluation procedures or they could be coupled with novel experimental designs to generate the biological data necessary to properly validate inferred networks.