Genetic targets for autism spectrum disorder identified by MU team
February 22, 2018
COLUMBIA, Mo. – Autism is a spectrum of closely related symptoms involving behavioral, social and cognitive deficits. Early detection of autism in children is key to producing the best outcomes; however, searching for the genetic causes of autism is complicated by various symptoms found within the spectrum. Now, a multi-disciplinary team of researchers at the […]
An RNAmazing research breakthrough
December 19, 2017
Professor of Bioengineering and the Dalton Cardiovascular Research Center Li-Qun (Andrew) Gu and Shi-Jie Chen, joint Professor of Physics, Biochemistry and the MU Informatics Institute and their team recently published “Nanopore electric snapshots of an RNA tertiary folding pathway,” in the prestigious journal Nature Communications.
Dr. Gregory Alexander recently awarded AHRQ grant.
October 9, 2017
Dr. Gregory Alexander, from The Sinclair School of Nursing, was recently awarded a $1,995,522.00 AHRQ grant. This grant will support an interdisciplinary research team who are already contributing to clinical research in long-term care settings. The PI is a doctorally-prepared RN and fellow in the American Academy of Nursing with over two decades of work […]
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.