Classifying Fiction and Non-Fiction Works Using Machine Learning

The objective of this project was to create a program that can determine whether an unknown text is a work of fiction or non-fiction using machine learning. Various datasets of speeches, ebooks, poems, scientific papers, and texts from Project Gutenberg and the Wolfram Example Data were utilized to train and test a Markov Chain machine learning model. A microsite was deployed with the final product that returns a probability of fictionality based on input from the user with 95% accuracy

Down With the Sickness: The Impact of Residential Living on the Spread of Illness

The health and wellbeing of students are extremely important, and with close quarters and prolonged contact in residential schools, we must consider the risk of contagious and infectious illnesses spreading faster and causing absences from classes. Residential living contributes to the learning environment at IMSA in multiple positive and impactful ways. However, it is important to discuss the potential health impacts that the increased exposure to other students can have on students and the absences these can cause. In this project, we will analyze the effects of residential living on the spread of illness and absences due to illness, especially during the flu outbreak of January 2018. In this project, we will use health records from the nurse and analyze them so we can see if there is a strong correlation between the spread of infectious diseases and residential living on campus. The data will help us answer questions about the number of students that have reported sick to the nurse\u27s office and if they went home due to their sickness or carried on with the school day

A Search for Inhibitors of HGXPRT Using 3D Molecular Docking

Malaria is a bloodborne disease primarily spread by mosquitoes. In 2019, there were an estimated 229 million cases of malaria worldwide, many of which were in third-world countries. Even though there are drugs that can combat malaria, they are not accessible to most consumers in need because of their high cost and, even if a consumer can access these drugs, they have low efficacy rates. HGXPRT is a purine salvage enzyme synthesized by malaria-causing parasites that is essential for their survival. Studies conducted in the past have concluded that neutralizing HGXPRT is an essential part of finding an affordable cure for malaria. This study focused on introducing different molecular compounds to HGXPRT to find a potential drug to combat malaria. Drug-like compounds from the Medicines for Malaria Venture’s Malaria Box were molecularly docked with the structure 3OZF to determine the potential for binding. Factors considered were Gibbs free energy, FullFitness, and positioning of the ligand relative to the 3OZF molecule compared to the original ligand, hypoxanthine. The goal of this study was to identify and prioritize specific compounds for further testing as potential drugs to combat malaria and to provide the scientific community with this data