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Causalmodels: An R Library For Estimating Causal Effects, Joshua Wolff Anderson
Causalmodels: An R Library For Estimating Causal Effects, Joshua Wolff Anderson
Computational and Data Sciences (MS) Theses
Free and open source software for statistical modeling and machine learning have advanced productivity in data science significantly. Packages such as SciPy in Python and caret in R provide fundamental tools for statistical modeling and machine learning in the two most popular programming languages used by data scientists. Unfortunately, robust tools similar to these are limited in terms of causal inference. The tools in R that exist lack consistent and standardized methodologies and inputs. R lacks a comprehensive package that offers traditional causal inference methods such as standardization, IP weighting, G-estimation, outcome regression, and propensity matching in one common package. …
An Information-Theoretic Analysis Of Adherence To Physical Exercise Routines, Lily Foster
An Information-Theoretic Analysis Of Adherence To Physical Exercise Routines, Lily Foster
Computational and Data Sciences (MS) Theses
One of the most common recommendations in healthcare is to simply form healthy habits, but little research has been done to understand the formation and continuation of a healthy habit that isn’t heavily influenced by an individual’s interpretation. Arizona State University’s WalkIT study aimed to analyze how goal setting and financial reinforcement can influence moderate-to-vigorous physical activity (MVPA) in adults, while using data from accelerometers to alleviate individual bias. In this trial, 512 insufficiently active adults were recruited to wear an accelerometer for 1 year and were then randomly assigned to one of the four study groups. Each group had …
Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter
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
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 …
Classifying Challenging Behaviors In Autism Spectrum Disorder With Neural Document Embeddings, Abigail Atchison
Classifying Challenging Behaviors In Autism Spectrum Disorder With Neural Document Embeddings, Abigail Atchison
Computational and Data Sciences (MS) Theses
The understanding and treatment of challenging behaviors in individuals with Autism Spectrum Disorder is paramount to enabling the success of behavioral therapy; an essential step in this process being the labeling of challenging behaviors demonstrated in therapy sessions. These manifestations differ across individuals and within individuals over time and thus, the appropriate classification of a challenging behavior when considering purely qualitative factors can be unclear. In this thesis we seek to add quantitative depth to this otherwise qualitative task of challenging behavior classification. We do so through the application of natural language processing techniques to behavioral descriptions extracted from the …