> *Dissertation Defense Announcement: > To: The George Mason University Community* > > *William Anderson von Canon > PhD Bioinformatics & Computational Biology Candidate > * > *Date: Tuesday April 12, 2011 > Time: 4:30 p.m. > Place: George Mason University > ** Occoquan Bldg. Room 203 > Prince William campus <http://www.gmu.edu/resources/visitors/findex.html> > > Dissertation Chair: Dr. Jeffrey Solka > Committee members: Dr. James D. Willett, Dr. Jason Kinser* > *Title: "Enhancement of Literature Based Discovery Using Advanced > Computational Techniques and Evaluation of Potential Discoveries > Related to Amyotrophic Lateral Sclerosis (ALS)" > * > 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: * > Literature based discovery (LBD) has been > used successfully to support analysis of medical literature and to identify > potential non related topics that could support novel discoveries in disease > research. As the concept of LBD expanded to the wider scientific community its > potential value grew but its statistical underpinnings needed further > refinement. The foundational research conducted by Don Swanson established a > potential link between "fish oil" and "Raynauds" disease by analyzing > literature inferences that linked the concepts indirectly through associated > words that were not common to the direct topic literature sources. The > identification of "joining words" that were common to each literature source > helped link the two concepts of "disease" and "potential cure" that otherwise > may not have been identified. This dissertation focused on enhancing LBD with > new probabilistic techniques that could increase the accuracy and efficiency of > discovering literature similarities while enforcing a more stringent > statistical foundation for the technique. The initial LBD research conducted by > Gordon/Dumais on Raynaud's disease, using the Latent Semantic Indexing (LSI) > technique, was recreated as part of this analysis. Two new statistical > techniques, Probabilistic Latent Semantic Analysis (PLSA) and Non-Negative > Matrix Factorization (NMF), were evaluated and supported increased precision in > intermediate literature identification and potential literature inferred > discovery over the original LSI technique. These techniques were then used to > conduct LBD on another novel disease, Amyotrophic Lateral Sclerosis (ALS). The > investigation of NMF and PLSA as new computation approaches did validate > quantifiable enhancements over the previous LSI techniques. While they enhanced > the level of accuracy and efficiency in both literature/ B /and/ C /discovery, the > aspect of being able to use each computational technique as reinforcement for > the other method's findings proved very interesting and will provide the > researcher stronger statistical LBD models in support of current and future > scientific discovery. > > ### >