> 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. > > ### > >