Identifying Heat-Resilient Corals Using Machine Learning and Microbiome

Due to global warming, coral reefs have been directly impacted with heat stress, resulting in mass coral bleaching. Within the coral species, some are more heat resistant, which calls for an investigation towards interventions that can enhance coral resilience for other heat-susceptible species. Studying heat-resistant corals’ microbial communities can provide a potential insight to the composition of heat-susceptible corals and how their resilience is achieved. So far, techniques to efficiently classify such vast microbiome data are not sufficient. In this paper, we present an optimal machine learning based pipeline for identifying the biomarker bacterial composition of heat-tolerant coral species versus heat-susceptible ones. Through steps of feature extraction, feature selection/engineering, and machine leaning training, we apply this pipeline on publicly available 16S rRNA sequences of corals. As a result, we have identified the correlation based feature selection filter and the Random Forest classifier to be the optimal pipeline, and determined biomarkers that are indicators of thermally sensitive corals.