Outstanding Graduate Research in Computational Science
1st. Prize: Non-Parallel Text Style Transfer with Self-Parallel Supervision
Rubio Liu (Computer Science) Advisor: Vosoughi
Style is the packaging in which writing is presented, often influencing the reader's impression of the text's content. Text style transfer models can unwrap a text's packaging and repackage the same content in a different style. However, the performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping exists between sentences of the source and target style; the style transfer models thus only receive weak supervision of the target sentences during training, which often leads the model to discard too much style-independent information, or utterly fail to transfer the style. In this work, we propose LaMer, a novel text style transfer framework based on large-scale language models. LaMer first mines the roughly parallel expressions in the non-parallel datasets with scene graphs, and then employs MLE training, followed by imitation learning refinement, to leverage the intrinsic parallelism within the data. On three benchmark datasets, LaMer shows superior performance than many baselines, and extensive human evaluations further confirm the effectiveness of LaMer.
2nd Prize: Hierarchical Tumor Immune Microenvironment Epigentic Deconvolution
Ze Zhang (Quantitative Biomedical Science, Epidemology) Advisor: Salas & Christensen
The complexity of the tumor microenvironment (TME) impedes the cost-effective deconvolution of TME cells with high resolution, accuracy, and specificity. We developed a novel tumor-type-specific hierarchical algorithm, HiTIMED, to deconvolve seventeen cell types in TME for twenty different carcinoma types using DNA methylation data in conjunction with the constrained projection quadratic programming approach. HiTIMED promises new avenues for the study of TME in assessing clinical outcomes. HiTIMED deconvolution is amenable to application in archival tumor biospecimens and provides a very cost-effective high-resolution cell composition profile enabling new opportunities to study the relation of the TME with etiologic factors, disease progression, and response to therapy.
Ze Zhang, John K. Wiencke, Karl T. Kelsey, Devin C. Koestler, Brock C. Christensen & Lucas A. Salas; HiTIMED: Hierarchical Tumor Immune Microenvironment Epigenetic Deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data; Cancer Research (Under review), May 2022
3rd Prize: Influence of ph on the Distribution and Evoluation of Lipid Cyclization Genes in Extremophiles
Laura Blum (Earth Sciences) Advisor: Leavitt
Single-celled Archaea are well-adapted to thrive in extreme environments on Earth, like hot springs. Optimal function of the cell membrane is crucial to survival in these high-stress environments, which reach the limits of temperature and pH conditions on Earth. We employed bioinformatics techniques to trace genes which form unique cell membrane lipid structures (grs) across hot springs environments. By analyzing genetic datasets from hot springs worldwide, we detected patterns in gene distribution associated with hot spring pH and temperature. Our results inform the relative importance of different environmental pressures in shaping the evolution of Archaeal lineages in diverse ecosystems.
Blum, L.N., Colman, D.R., Eloe-Fadrosh, E.A., Kellom, M., Boyd, E.S., Zhaxybayeva, O., Leavitt, W.D. (2022, May 19). Distribution of GDGT Membrane Lipid Cyclization Genes in Terrestrial Thermal Springs Linked to pH. [Conference Talk]. AGU AbSciCon, Atlanta, GA, U.S.