Applications of Machine Learning Algorithms for Coral Disease Fate in Caribbean Corals
The Caribbean is known as a coral disease “hot spot” due to the high prevalence of acute and chronic diseases that have plagued corals in the area. Two diseases, Stony Coral Tissue Loss Disease (SCTLD) and White Plague (WP), are common and infect many coral species. These two diseases have been studied in a genotype- matching study that looked at transcriptomics of baseline, and post-exposure to disease in four species of corals. While transcriptomic studies have improved our knowledge of host response, a knowledge gap regarding the disease risk corals have prior to disease exposure still exists. Understanding disease risk before an outbreak is an important step in modeling disease dynamics of corals as it will help conservation efforts and disease response protocols. One way to identify disease risk is the application of machine learning to identify patterns of expression based on disease outcome. By applying novel but proven layers of machine learning programs from medical research and using healthy corals whose disease fate are known, we can identify which biological processes are relevant to disease susceptibility. We examined six different types of machine learning algorithms for detection of presence/absence of genes and expression patterns correlated o whether the coral got disease when exposed or not. We will report what types of data these algorithms provide and how it can be applied for disease motoring and modeling.