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Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889194780 Year: Pages: 191 DOI: 10.3389/978-2-88919-478-0 Language: English
Publisher: Frontiers Media SA
Subject: Biotechnology --- General and Civil Engineering --- Genetics --- Science (General)
Added to DOAB on : 2016-03-10 08:14:33
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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.

Computational Systems Biology of Pathogen-Host Interactions

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889198214 Year: Pages: 198 DOI: 10.3389/978-2-88919-821-4 Language: English
Publisher: Frontiers Media SA
Subject: Microbiology --- Science (General)
Added to DOAB on : 2016-01-19 14:05:46
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A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Therefore, the analysis of infection mechanisms through PHIs is indispensable to identify diagnostic biomarkers and next-generation drug targets and then to develop strategic novel solutions against drug-resistance and for personalized therapy. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we bring together studies on the below listed sections to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions:- Computational Inference of PHI Networks using Omics Data- Computational Prediction of PHIs- Text Mining of PHI Data from the Literature- Mathematical Modeling and Bioinformatic Analysis of PHIs Computational Inference of PHI Networks using Omics Data Gene regulatory, metabolic and protein-protein networks of PHI systems are crucial for a thorough understanding of infection mechanisms. Great advances in molecular biology and biotechnology have allowed the production of related omics data experimentally. Many computational methods are emerging to infer molecular interaction networks of PHI systems from the corresponding omics data. Computational Prediction of PHIs Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. Therefore, computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest. Text Mining of PHI Data from Literature Despite the recent development of many PHI-specific databases, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature. Mathematical Modeling and Bioinformatic Analysis of PHIs After the reconstruction of PHI networks experimentally and/or computationally, their mathematical modeling and detailed computational analysis is required using bioinformatics tools to get insights on infection mechanisms. Bioinformatics methods are increasingly applied to analyze the increasing amount of experimentally-found and computationally-predicted PHI data. Acknowledgements: We, editors of this e-book, acknowledge Emrah Nikerel (Yeditepe University, Turkey) and Arzucan Özgür (Bogaaziçi University, Turkey) for their contributions during the initiation of the Research Topic.

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