Development of an Algorithmic Model to Reduce Memory and Learning Deficits on Trisomic Mice

Abstract

For many decades, scientists have been interested in finding cures for diseases due to the human genome. Down Syndrome is the most common genetic disorder and one of the most common causes of learning and memory deficits. It is a condition that has long preoccupied the scientific community, with the finding of treatments being limited to the individual conditions caused by the Syndrome. It is easy to see that finding a cure for Down Syndrome requires systematic and long-term research. Scientists will need to use whatever means at their disposal, with technological aids being the ones of most interest. Artificial Intelligence has managed to offer much to humanity, despite the short time that has elapsed since its appearance on the scientific firmament. Its discoveries in every scientific field have paved the way for the solution of problems that have occupied scientists for years, while her entry into our daily lives has managed to facilitate her to a great extent. This research aims to introduce Machine Learning (ML) models capable to improve the memory and to reduce the learning deficits of mice with Down Syndrome, that were used as experimental animals. The experimental datasets were developed by Higuera et al., 2015 and Ahmed et al., 2015, and they are related to the levels of proteins present in the brainstem of mice. They are publicly available, by the UCI (University of California Irvine) Open Database. The emerged ML models are very promising and they deserve further attention by the scientific community.