240 Naka Hall
PROTEIN TRANSPORT: BIOINFORMATICS METHODS FOR UNDERSTANDING PROTEIN SUBCELLULAR LOCALIZATION
Eukaryotic cells contain diverse subcellular organelles. These organelles form distinct
functional cellular compartments where different biological processes and functions are
carried out. The accurate translocation of a protein is crucial to establish and maintain
cellular organization and function. Newly synthesized proteins are transported to different
cellular components with the assistance of protein transport machineries and complex
targeting signals. Mis-localization of proteins is often associated with metabolic disorders
and diseases. Compared with experimental methods, computational prediction of protein
localization, utilizing different machine learning methods, provides an efficient and
effective way for studying the protein subcellular localization on the whole-proteome level.
Here, we present in this dissertation the bioinformatics methods for studying protein
subcellular localization. We reviewed the studies of protein subcellular transport and
machine learning methods in bioinformatics, presented our work on mitochondrial protein
targeting prediction in plants, summarized the ongoing development of a web-resource for
protein subcellular localization, and discussed the future work and development.
M212 Medical Science Building
Informatics framework for the identification of diagnostic discrepancies and errors
103 Animal Science Research Center
HOMOLOGY SEQUENCE ANALYSIS USING GPU ACCELERATION
2206A Student Center
Using Deep learning method (CNN) for prediction of ubiquitination protein
Ubiquitination, as a post-translational modification, is a crucial biological process presented in cell signaling, death and localization. Identification of ubiquitination protein is of fundamental importance for understanding molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins remain undiscovered, even in well studied model organisms. To reduce experimental costs, computational (in silico) methods have been introduced to predict ubiquitination sites. If we can predict whether a query protein can be ubiquitinated or not, it is meaningful by itself and helpful for predicting ubiquitination sites. However, all the computational methods so far only predict ubiquitination sites, with unsatisfactory accuracy. In this study, we developed the deep learning method with CNNarchitecture in Pytorch environment for predicting ubiquitination proteins without relying on ubiquitination site prediction.
S304 Memorial Union
Automation of Volumetric Analysis of Adiposity in Canines
Roughly 30-40% of all dogs and cats that are seen by a veterinarian can be classified as obese. Despite this, veterinary practices still utilize a 5 point or 9 point subjective classification system when classifying patients as obese, which can provide difficult when providing accurate nutritional consults to veterinary clients aiming to decrease their pet’s weight. Further, the obesity itself can lead to worsening of comorbid conditions. Thus, an automation of the process of assessing adiposity through CT scan was attempted, looking specifically at the thoracic region of the animal. First, the issues with the current BCS system were highlighted through the manual analysis of thoracic body fat in comparison to assigned BCS scores, with a focus on percent adiposity. Next, this process will be automated to analyze the adiposity of the thoracic region so it does not need to be done by hand for CT scans. To accommodate the average practitioner, this method will be applied en masse to radiographs through the comparison of animals who received radiographs and CT scans on the same day, and mathematically correlated to allow for x-ray utility for this tool. Finally, this will be applied to a variety of diseases to assess if there is a threshold adiposity level at which animals are at risk for a worsened prognosis.
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