> 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 > > Title: "A COMPUTATIONAL CLASSIFICATION SYSTEM FOR DISEASE-CAUSING SINGLE AMINO ACID POLYMORPHISMS" > > 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: > 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. > > ### >