2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2013) PDF Download
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Author:
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ISBN: 9781479934614
Category : Bioinformatics
Languages : en
Pages : 0
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Book Description
Author:
Publisher:
ISBN: 9781479934614
Category : Bioinformatics
Languages : en
Pages : 0
Get Book Here
Book Description
Author:
Publisher:
ISBN: 9781479934614
Category :
Languages : en
Pages : 105
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Book Description
Author:
Publisher:
ISBN: 9781479934621
Category : Bioinformatics
Languages : en
Pages : 105
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Author:
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ISBN:
Category : Genomics
Languages : en
Pages :
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Author: Institute of Electrical and Electronics Engineers
Publisher:
ISBN: 9781424403851
Category :
Languages : en
Pages :
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Author:
Publisher:
ISBN: 9781424409990
Category : Genomics
Languages : en
Pages :
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Author:
Publisher:
ISBN:
Category : Genomics
Languages : en
Pages : 37
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Author: Jiaqian Wu
Publisher: Springer
ISBN: 9401774501
Category : Science
Languages : en
Pages : 190
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Book Description
This volume focuses on modern computational and statistical tools for translational gene expression and regulation research to improve prognosis, diagnostics, prediction of severity, and therapies for human diseases. It introduces some of state of the art technologies as well as computational and statistical tools for translational bioinformatics in the areas of gene transcription and regulation, including the tools for next generation sequencing analyses, alternative spicing, the modeling of signaling pathways, network analyses in predicting disease genes, as well as protein and gene expression data integration in complex human diseases etc. The book is particularly useful for researchers and students in the field of molecular biology, clinical biology and bioinformatics, as well as physicians etc. Dr. Jiaqian Wu is assistant professor in the Vivian L. Smith Department of Neurosurgery and Center for Stem Cell and Regenerative Medicine, University of Texas Health Science Centre, Houston, TX, USA.
Author: Guotong Zhou
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Benjamin Haibe-Kains
Publisher: Frontiers Media SA
ISBN: 2889194787
Category : Bioengineering
Languages : en
Pages : 192
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Book Description
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.
