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> Dissertation Defense Announcement:
> To:  The George Mason University Community
>
> *Candidate: Nuttachat Wisittipanit
> Program: PhD Bioinformatics & Computational Biology 
> *
> *Date:   Monday March 12, 2012
> Time:   10:00 a.m. 
> Place:  George Mason University
> ** 	     Research Bldg, Room 161
> 	     Fairfax campus <http://www.gmu.edu/resources/visitors/findex.html>
>   
> *Dissertation Director: Dr. Huzefa Rangwala
> Committee Chair: Dr. Patrick Gillevet
> Committee Members: Dr. Jason Kinser, Dr. Dmitri Klimov
>
> Title: "MACHINE LEARNING APPROACH FOR PROFILING HUMAN MICROBIOME" 
> **
> The dissertation is on reserve in the Johnson Center Library, Fairfax campus.
> The doctoral project will not be read at the meeting, but should be read in advance. 
> /**/All members of the George Mason University community are invited to attend.
>
>
> ABSTRACT:
>
> Understanding and characterizing the roles and variation of the 
> microbial organisms living within the human host is the main goal of 
> the international human microbial research community. These human 
> flora has shown the ability to harvest otherwise inaccessible 
> nutrients, synthesize vitamins and speed up the gut epithelial cell 
> renewal process. Successful characterization of the microbial 
> communities will have enormous impact towards improved human 
> nutrition, immunity, and medical treatments. The advent of highly 
> parallel Next Generation DNA sequencing technologies (NGS) makes it 
> possible to determine the genomic content of not only individual 
> organisms, but entire pools of co-existing species. Sequencing of 
> microbial organisms collectively, is referred to as metagenomic and 
> the comprehensive characterization of microbes within the human host 
> is referred to as the "microbiome". Using advanced computational 
> approaches, information about the microbial functions and community 
> characterizations can be elucidated from those complex data.
>
> The research in this area is spearheaded by the Human Microbiome 
> Project (HMP) initiative at the National Institute of Health. The main 
> goal of HMP is to generate resources that enable the comprehensive 
> characterization of the human microbiota and analysis of its role in 
> human health and disease. By combining both the NGS technologies and 
> traditional approaches for functional analysis, HMP will lay the 
> foundation for further studies of human-associated microbial 
> communities. The analysis of the deluge of information from the HMP 
> and other similar efforts has opened a new field of microbiome 
> research that is in severe need of computational tools suitable for 
> many areas of data analysis. The general objective of this 
> dissertation is to develop advanced computational tools specialized in 
> the human microbiome research to characterize the microbial community 
> based on sequence data, visualize the community patterns and discover 
> significant correlations between microbial taxa and clinical diseases. 
> These tools are developed using machine learning approaches that are 
> appropriate for analyzing complex microbiome-related datasets due to 
> their ability to extract underlying information from the large and 
> noisy data produced in this research field. In this dissertation, we 
> specifically develop machine learning tools for microbiome analysis. 
> The suite of tools includes classification and clustering approaches 
> coupled with an easy-to-use web interface made publicly available. It 
> is hoped that this work will provide a foundation for further analysis 
> of the microbiome.
>
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