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>> Reminder: 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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