Other Statistics and Probability Commons^™

Existing And Potential Statistical And Computational Approaches For The Analysis Of 3d Ct Images Of Plant Roots, Zheng Xu, Camilo Valdes, Jennifer Clarke

Department of Statistics: Faculty Publications

Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge. We review statistical and computational approaches that have been …

Characterization Of Soybean Protein Adhesives Modified By Xanthan Gum, Chen Feng, Fang Wang, Zheng Xu, Huilin Sui, Yong Fang, Xiaozhi Tang, Xinchun Shen

Department of Statistics: Faculty Publications

The aim of this study was to provide a basis for the preparation of medical adhesives from soybean protein sources. Soybean protein (SP) adhesives mixed with different concentrations of xanthan gum (XG) were prepared. Their adhesive features were evaluated by physicochemical parameters and an in vitro bone adhesion assay. The results showed that the maximal adhesion strength was achieved in 5% SP adhesive with 0.5% XG addition, which was 2.6-fold higher than the SP alone. The addition of XG significantly increased the hydrogen bond and viscosity, as well as increased the β-sheet content but decreased the α-helix content in the …

Development Of 11-Plex Mol-Pcr Assay For The Rapid Screening Of Samples For Shiga Toxin-Producing Escherichia Coli, Travis A. Woods, Heather M. Mendez, Sandy Ortega, Xiaorong Shi, David Marx, Jianfa Bai, Rodney A. Moxley, T. G. Nagaraja, Steven W. Graves, Alina Deshpande

Department of Statistics: Faculty Publications

Strains of Shiga toxin-producing Escherichia coli (STEC) are a serious threat to the health, with approximately half of the STEC related food-borne illnesses attributable to contaminated beef. We developed an assay that was able to screen samples for several important STEC associated serogroups (O26, O45, O103, O104, O111, O121, O145, O157) and three major virulence factors (eae, stx1, stx2) in a rapid and multiplexed format using the Multiplex oligonucleotide ligation-PCR (MOL-PCR) assay chemistry. This assay detected unique STEC DNA signatures and is meant to be used on samples from various sources related to beef production, providing a multiplex and high-throughput …

Application Of Transfer Learning For Cancer Drug Sensitivity Prediction, Saugato Rahman Dhruba, Raziur Rahman, Kevin Matlock, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Background: In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context.

Results: In this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first …

Investigation Of Model Stacking For Drug Sensitivity Prediction, Kevin Matlock, Carlos De Niz, Raziur Rahman, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Background: A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types.

Results: We explore the predictive performance of model stacking and the effect of stacking on the predictive bias and squarred error. In addition we discuss the analytical underpinnings supporting the advantages of stacking in reducing …