A Machine Learning Analysis of Metabolites and Other Molecular Changes Causing Glaucoma
This study will analyze hundreds of thousands of concomitant molecular alterations to identify those that reflect physiological processes that end up causing glaucoma. One of the main aims is to highlight which changes of metabolism and methylation modifications of the DNA are associated with glaucoma or intraocular pressure elevations. Because there are such potential changes, finding out which are important to glaucoma, or intraocular pressure control, is an arduous task that can only be completed through newly available machine learning techniques. These techniques can discover patterns hidden among many variables, which would take the human eye an impossibly long time to detect.
This project will be the first large-scale mechanism of biological components and factors that are known to fluctuate as a result of external (environmental) factors and potentially more amenable to pharmacological intervention. This project will allow us to understand better what happens to the biological processes that start from the genes, are modified by unknown factors in the cell environment, and end up causing glaucoma. Many of these intermediate biological links have been studied and may be modified by chemicals, which, if identified, can be of potential use to treat glaucoma. This project will most likely improve our understanding of the molecular cascades that develop high intraocular pressure and glaucoma in patients. It will identify molecular mediators that vary during the life course and are likely to be influenced by chemical compounds. This project will search the pool of known substances to identify those known to cause biological changes that would counter the changes associated with glaucoma.