Physical Sciences and Mathematics Commons^™

Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter

Computational and Data Sciences (MS) Theses

Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …

Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques, Matthew J. Nwerem

Computational and Data Sciences (MS) Theses

Computational investigation of molecular structures and reactions of biological and pharmaceutical interests remains a grand scientific challenge due to the size and conformational flexibility of these systems. The work requires parsing and analyzing thousands of conformations in each molecular state for meaningful chemical information and subjecting the ensemble to costly quantum chemical calculations. The current status quo typically involves a manual process where the investigator must look at each conformation, separating each into structural families. This process is time-intensive and tedious, making this process infeasible in some cases, and limiting the ability of theoreticians to study these systems. However, the …

Multi-Modal Data Fusion, Image Segmentation, And Object Identification Using Unsupervised Machine Learning: Conception, Validation, Applications, And A Basis For Multi-Modal Object Detection And Tracking, Nicholas Lahaye

Computational and Data Sciences (PhD) Dissertations

Remote sensing and instrumentation is constantly improving and increasing in capability. Included within this, is the increase in amount of different instrument types, with various combinations of spatial and spectral resolutions, pointing angles, and various other instrument-specific qualities. While the increase in instruments, and therefore datasets, is a boon for those aiming to study the complexities of the various Earth systems, it can also present a large number of new challenges. With this information in mind, our group has set our aims on combining datasets with different spatial and spectral resolutions in an effective and as-general-as-possible way, with as little …

Applications Of Machine Learning To Facilitate Software Engineering And Scientific Computing, Natalie Best

Computational and Data Sciences (PhD) Dissertations

The use of machine learning has risen in recent years, though many areas remain unexplored due to lack of data or lack of computational tools. This dissertation explores machine learning approaches in case studies involving image classification and natural language processing. In addition, a software library in the form of two-way bridge connecting deep learning models in Keras with ones available in the Fortran programming language is also presented.

In Chapter 2, we explore the applicability of transfer learning utilizing models pre-trained on non-software engineering data applied to the problem of classifying software unified modeling language diagrams where data is …