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.
2206A Student Center
Effects of Pain Management Clinical Decision Support in an Inpatient Setting for Patients Experiencing Abdominal Pain
Disorganization of pain-management-related information in an EMR may limit clinicians’ ability to consider clinical factors comprehensively. A clinical decision support (CDS) system for pain management was developed and deployed at University of Missouri Healthcare. CDS effects were examined for inpatients with diagnoses of diverticulitis, pancreatitis, and abdominal pain. Statistically significant differences were found in the average NRS-11 self-reported pain scores with a mean reduction of 0.7, and number of pain related medications prescribed, with a mean reduction of 1.2 pain medication orders per day. No statistical correlation was found between the use of the CDS and prescription of different classes of pain medications at discharge, nor with the use of naloxone for reversing overdose. Nurses were the primary user of the system, with 79.3% of all CDS views. Patients may benefit with overall reduced pain scores and improved medication management with the use of this CDS.
Laritza Rodriguez, M.D., Ph.D.Date:
2206 ABC Student Center
Use of Powerful Tools for Meaningful Conclusions from Sparse Data
At any given time, over 10 million women are pregnant or lactating in the United States, about 80% of these pregnancies result in a normal pregnancy and life birth. The remaining are associated with a wide range of pregnancy related diseases, an even lower percent of patients present with complications not related to the pregnancy itself. The size of the data is at first glance exciting for the informatics researcher however, the low incidence of positive cases of each type of disease results in sparse data difficult to analyze resulting in less than ideal models for data mining and knowledge extraction. During the seminar I will illustrate the relevance of pregnancy related research focusing then on the demonstration of how data manipulation, manual annotation, the use of publicly available tools make it possible to extract useful information and draw valid conclusions with well-known machine learning algorithms.
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