> Dissertation Defense Announcement:
> To:  The George Mason University Community
> *Candidate: Baris E. Suzek
> Program: PhD Bioinformatics & Computational Biology 
> *
> *Date:   Tuesday November 27, 2012
> Time:   11:00 A.M. - 1:00 P.M. 
> Place:  George Mason University
> ** 	    Occoquan Bldg. Room #110-A
> 	    Prince William campus <http://www.gmu.edu/resources/welcome/Directions-to-GMU.html>
> *Dissertation Chair: Dr. Iosif Vaisman
> Committee Members: Dr. Donald Seto, Dr. James D. Willett
> 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.
> The advances in high-throughput sequencing and molecular profiling 
> techniques led to identification of single amino acid polymorphisms 
> (SAPs). The large volume of SAPs makes it time and cost prohibitive to 
> experimentally test and validate the disease causing or pathogenic 
> impacts for all of them individually. Hence, accurate computational 
> methods to classify disease-causing SAPs are needed and they can play 
> a clinical role as a core part of a system to support clinicians' 
> decision making for diagnosis.  This thesis describes several 
> components that are collectively used for a pipeline to classify 
> disease-causing SAPs for human.  These components include (1)  a data 
> collection and integration pipeline that collects a set of SAPs, 
> sequence and structure information (2) a non-redundant protein 
> sequence cluster database that is employed in assessing evolutionary 
> conservation of  amino acids (3) a methodology that use evolutionary 
> conservation information to classify disease causing SAPs, and  (4) an 
> ensemble classifier that combines several machine learning classifiers 
> for different sequential and structural contexts SAPs are located on. 
> This approach is comprehensive; it uses several sequence annotation 
> and structure information resources to provide sequential and 
> structural context to SAPs and create features for classifiers. It is 
> extensible; the resources and classifiers can be updated as new 
> sequence annotations or structure information become available. It is 
> specific; it assesses impact of SAP within sequential and structural 
> context, utilizing a unique blend of evolutionary information.  The 
> resulting classification system performs comparable to or better than 
> existing SAP classifiers. In long run, this system is intended to 
> become a core part of a system to support clinicians' decision making 
> for diagnosis, identification of treatment protocols and preventive care.
>   ###