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Full-Text Articles in Life Sciences
An Evolutionary Comparative Study Of Congenital Stationary Night Blindness-Associated Trpm1 Genetic Variants Of Uncertain Significance In Horses And Humans Utilizing Caenorhabditis Elegans, Gabrielle Davis
Theses
Congenital stationary night blindness (CSNB) is a heterogeneous collection of genetic diseases affecting the eyes and vision in horses and humans. Current research has implicated several genetic mutations impacting different genes involved in phototransduction and signal transmission, including TRPM1. In horses, genetic mutations in TRPM1 also result in a leopard spotting pattern or leopard complex. The goal of this study is to examine the potential impact of CSNB associated TRPM1 missense variants of uncertain significance (VUS). Previous research in Caenorhabditis elegans have revealed an orthologous TRPM1 gene known as gon-2 that allows for comparative studies. The evolutionary relationship of …
The Role Of Software Engineering In Bioinformatics, Brendan Sean Lawlor
The Role Of Software Engineering In Bioinformatics, Brendan Sean Lawlor
Theses
This thesis proposes that by applying state-of-the-art software engineering tools, techniques and frameworks to currently recognised challenges in bioinformatics, improved outcomes can be attained in that field. It begins by decomposing software engineering into two categories, namely process and architecture, and choosing two key challenges in the practice of bioinformatics: reproducibility and scalability. The body of the thesis is an exploration of the intersection between these two software engineering categories and these two bioinformatics challenges. The question is asked: Can best practices in professional software engineering be applied to address key issues in the bioinformatics domain, creating positive outcomes? And …
New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger
New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger
Theses
Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …