The model was successful in classifying in situ corals with either white plague or SCTLD, with a classification rate of 98.95% (AUC) and an overall error rate of 0.0754. 🥳

Using coral gene expression data, we identified biomarkers through logistic regression and differential expression. We built a Partial Least Squares Discriminant Analysis (PLS-DA) model that reliably distinguishes between WP and SCTLD, which was tested and validated using in situ samples.

Excited to be presenting at Ocean Sciences #OSM24 this year! If you want to hear about innovative ways to leverage machine learning in genetic expression studies to classify and characterize coral disease come to my talk tomorrow!