Thesis (doctoral)--Universität des Saarlandes, 2006. / Includes bibliographical references (p. 327-350).

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Raymond J. Mooney. Vita. Includes bibliographical references. Also available from UMI.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 43-44). Also available in electronic version. Access restricted to campus users.

Thesis (M.S.)--Brigham Young University. Dept. of Statistics, 2008. / Includes bibliographical references (p. 54-57).

Thesis (Ph.D.)--Drexel University, 2010. / Includes abstract and vita. Includes bibliographical references (leaves 141-150).

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 100-106). Also available in electronic version.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Thesis (Ph.D.)--University of Texas at Dallas, 2007. / Includes vita. Includes bibliographical references (leaves 154-160)

In human-human dialogues, face-to-face meetings are often preferred over phone conversations.One explanation is that non-verbal modalities such as gesture provide additionalinformation, making communication more efficient and accurate. If so, computerprocessing of natural language could improve by attending to non-verbal modalitiesas well. We consider the problem of sentence segmentation, using hand-annotatedgesture features to improve recognition. We find that gesture features correlate wellwith sentence boundaries, but that these features improve the overall performance of alanguage-only system only marginally. This finding is in line with previous research onthis topic. We provide a regression analysis, revealing that for sentence boundarydetection, the gestural features are largely redundant with the language model andpause features. This suggests that gestural features can still be useful when speech recognition is inaccurate.

Recent innovations and progress in machine learning algorithms from the Natural Language Processing (NLP) community have motivated efforts to apply these models and concepts to proteins. The representations generated by trained NLP models have been shown to capture important semantic and structural understanding of proteins encompassing biochemical and biophysical properties, among other key concepts. In turn, these representations have demonstrated application to protein engineering tasks including mutation analysis and design of novel proteins. Here we use this NLP paradigm in a protein engineering effort to further red shift the emission wavelength of the red fluorescent protein mNeptune684 using only a small number of functional training variants ('Low-N' scenario). The collaborative nature of this thesis with the Department of Chemistry and Biomolecular Sciences explores using these tools and methods in the rational design process.