The etiology of central nervous system tumors (CNSTs) is mainly unknown. Aside from extremely rare genetic conditions, such as neurofibromatosis and tuberous sclerosis, the only unequivocally identified risk factor is exposure to ionizing radiation, and this explains only a very small fraction of cases. Using meta-analysis, gene networking and bioinformatics methods, this dissertation explored the hypothesis that environmental exposures produce genetic and epigenetic alterations that may be involved in the etiology of CNSTs. A meta-analysis of epidemiological studies of pesticides and pediatric brain tumors revealed a significantly increased risk of brain tumors among children whose mothers had farm-related exposures during pregnancy. A dose response was recognized when this risk estimate was compared to those for risk of brain tumors from maternal exposure to non-agricultural pesticides during pregnancy, and risk of brain tumors among children exposed to agricultural activities. Through meta-analysis of several microarray studies which compared normal tissue to astrocytomas, we were able to identify a list of 554 genes which were differentially expressed in the majority of astrocytomas. Many of these genes have in fact been implicated in development of astrocytoma, including EGFR, HIF-1α, c-Myc, WNT5A, and IDH3A. Reverse engineering of these 554 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I-IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme (GBM) were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. Lastly, bioinformatics analysis of environmental databases and curated published results on GBM was able to identify numerous potential pathways and gene-environment interactions that may play key roles in astrocytoma development. Findings from this research have strong potential to advance our understanding of the etiology and susceptibility to CNSTs. Validation of our 'key genes' and pathways could potentially lead to useful tools for early detection and novel therapeutic options for these tumors.

<div>For a long time, the collection of data through sensors and other means was seen as inconsequential. However, with the somewhat recent developments in the areas of machine learning, data science, and statistical analysis, as well as in the rapid growth of computational power being allotted by the ever-expanding computer industry, data is not just being seen as secondhand information anymore. Data collection is showing that it currently is and will continue to be a major driving force in many applications, as the predictive power it can provide is invaluable. One such area that could benefit dramatically from the use of predictive techniques is the steel industry. This thesis applied several machine learning techniques to predict steel deformation issues collectively known as the hook index problem [1].</div><div><br></div><div>The first machine learning technique utilized in this endeavor was neural networking. The neural networks built and tested in this research saw the use of classification and regression prediction models. They also implemented the algorithms of gradient descent and adaptive moment estimation. Through the employment of these networks and learning strategies, as well as through the line process data, regression-based networks made predictions with average percent error ranging from 106-114%. In similar performance to the regression-based networks, classification-based networks made predictions with average accuracy percentage ranges of 38-40%.</div><div><br></div><div>To remedy the problems relating to neural networks, Bayesian networking techniques were implemented. The main method that was used as a model for these networks was the Naïve Bayesian framework. Also, variable optimization techniques were utilized to create well-performing network structures. In the same vein as the neural networks, Bayesian networks used line process data to make predictions. The classification-based networks made predictions with average accuracy ranges of 64-65%. Because of the increased accuracy results and their ability to draw causal reasoning from data, Bayesian networking was the preferred machine learning technique for this research application.</div>