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Multi-dimensional scaling and MODELLER based evolutionary algorithms for protein model refinement
(University of Missouri--Columbia, 2013)
To computationally obtain an accurate prediction of the three-dimensional structure of a protein from its primary sequence is one of the most important problems in bioinformatics and has been actively researched for many years. Although a number...
AMD, analysis of mood dysregulation : a machine learning approach
(University of Missouri--Columbia, 2016)
There is a popular saying, "Stress kills." This statement can be true with repeated exposures to psychological mood dysregulation, which can lead to or worsen stress related conditions such as heart disease and cancer. ...
Deep learning for small object detection in images
(University of Missouri--Columbia, 2020)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] With the rapid development of deep learning in computer vision, especially deep convolutional neural networks (CNNs), significant advances have been made in recent years...
Applications of deep neural networks to protein structure prediction
(University of Missouri--Columbia, 2018)
Protein secondary structure, backbone torsion angle and other secondary structure features can provide useful information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity ...
Computational protein structure prediction using deep learning
(University of Missouri--Columbia, 2020)
Protein structure prediction is of great importance in bioinformatics and computational biology. Over the past 30 years, many machine learning methods have been developed for this problem in homology-based and ab-initio approaches. Recently, deep...