Meet Polygence Scholars and explore their projects
Project: “Comparative Study of LSTM and T5-Flan Models for Automated Test Case Generation from Gherkin Requirements“
Project: “Does a summer literacy experience that uses diverse literature promote engagement and enjoyment in summer reading among elementary school students?“
Project: “How can muscles like the ACL be prevented from tearing, and in the chance it is torn, how can rehabilitation help an athlete get back to their sport quicker?“
Project: “To treat Pancreatic Cancer in the modern age, ASOs (Antisense Oligonucleotides) need to be readily utilized and researched due to the promising research already available.“
Project: “(To be added by Jason)“
Project: “To what extent does the presence of kelp forests mitigate the impacts of anthropogenic ecological threats in the region, such as ocean acidification and nutrient runoff?“
Project: “Molecular mechanisms of different tauopathies“
Project: “The Detrimental Impacts of Supermarket Redlining: How the Redistribution of Wasted Food Can Alleviate Food Insecurity“
Project: “Combating Fake News“
Project: “Current Treatments and Advancements in Asthma Therapeutics“
Project: “Differentiating Methods of Keratoconus Diagnosis Utilizing AI“
Project: “An Overview of Pulsars and XNAV: Modern Astrophysics' Interstellar GPS“
Project: “Genetic therapeutics to treat Alzheimer's disease and how they compare to current therapies“
Project: “Relationship between economic stress and alcohol addiction in Kazakhstan“
Project: “Can a neural network determine whether or not a person has Parkinson's Syndrome?“
Project: “Endocrine Disrupting Chemicals (EDCs) in the Gulf of Thailand : A Review and Identifying Knowledge Gaps“
Project: “Investigating the Driver of the Scatter in the Star Forming Sequence“
Project: “Bots Across Borders: Analyzing AI Deployment Across Hospital Wealth Levels“
Project: “Is there an association between a high dietary glycemic index/glycemic load and the risk of developing gynaecological cancer in women?“
Project: “Predicting Excess Bond Returns using Neural Networks“