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 …

Machine Learning Methods For Depression Detection Using Smri And Rs-Fmri Images, Marzieh Sadat Mousavian

LSU Doctoral Dissertations

Major Depression Disorder (MDD) is a common disease throughout the world that negatively influences people’s lives. Early diagnosis of MDD is beneficial, so detecting practical biomarkers would aid clinicians in the diagnosis of MDD. Having an automated method to find biomarkers for MDD is helpful even though it is difficult. The main aim of this research is to generate a method for detecting discriminative features for MDD diagnosis based on Magnetic Resonance Imaging (MRI) data.

In this research, representational similarity analysis provides a framework to compare distributed patterns and obtain the similarity/dissimilarity of brain regions. Regions are obtained by either …

Ensemble Protein Inference Evaluation, Kyle Lee Lucke

Graduate Student Theses, Dissertations, & Professional Papers

The Protein inference problem is becoming an increasingly important tool that aids in the characterization of complex proteomes and analysis of complex protein samples. In bottom-up shotgun proteomics experiments the metrics for evaluation (like AUC and calibration error) are based on an often imperfect target-decoy database. These metrics make the inherent assumption that all of the proteins in the target set are present in the sample being analyzed. In general, this is not the case, they are typically a mix of present and absent proteins. To objectively evaluate inference methods, protein standard datasets are used. These datasets are special in …