## 3 Results and Discussion
### Coral Health Predictions from Field Data

Table 4: Subset of host coral (OFAV) and Symbiodiniaceae (SYMBOF) proteins featured in proteomic predictive modeling analyses. All \(e\)-values were \(<\)10\({}^{-80}\), and the top hit was always a scleractinian coral for the host (typically _O. faveolata_ or _Acropora millpora_). The “model total effect” was derived from a dependent resampled inputs analysis. AB = actively bleaching. CHD = colony health designation. HC = healthy controls. NA = not applicable. at which point 2 DHWs had already accrued (Figure 1); while visibly healthy, these corals may already have actually been sub-lethally stressed. For this reason, it was necessary to build a large number of additional models in which various proportions of corals of differing colony health designations of different genotypes sampled at different reefs on different dates were featured as training, validation, and test samples (Table 5). The overall goal was to effectively \"trick\" the AI to reduce the chances of over-fitting, a significant concern with neural networks in particular, since the AI will repeatedly \"comb\" the predictors to try and find combinations (& under varying degrees, levels, & types of activation) that lead to an accurate solution. With training data alone, virtually all neural networks tested yielded models with 100% accuracy. When holding back data from validation and/or test samples (i.e., those whose data were not featured in the first incarnation of the model), accuracies (Table 5) ranged from 85-95% (depending on how the samples were partitioned among training, validation, & test samples). In other words, if 100 coral samples were given to a technician who had no knowledge of their stress tolerance, and the technician quantified the 16-28 proteins used in the models generated herein, he/she would accurately predict the bleaching likelihood of 85-95 of the associated coral colonies. It is important to emphasize that, because colony health designation is a property of the coral colony, it was necessary to ensure that the AI could accurately predict this trait regardless of the biopsy's phenotype. Although a bleaching-resistant coral would never yield a bleached biopsy (Figure 2d), a bleaching-susceptible coral could be associated with a healthy one (Figure 2e), and a healthy biopsy from such a colony could fundamentally differ from that of a bleaching-resistant colony (despite both being \"healthy\"); indeed, many have shown that many such corals display \"stress-hardened\" proteomes [17] and transcriptomes [18] to cope with life in marginal environments. This is why the modeling types of Table 5 are amongst the most computationally intensive in existence. In at least one instance, however, ML was not required; in the model CHD-field\(\blacktriangleright\)CHD-field(c), in which only pre- and post-bleaching samples were used for training, validation, and testing, gen-reg with a lasso algorithm was sufficient to accurately predict bleaching susceptibility (95%). Unlike neural networks, response variable reduction is possible with gen-reg, and the model featured only two proteins: the DELTA activoxin described above and a Symbiodiniaceae chromosome segregation protein (SMC; accession: SYMBOF_DN75265_c0_g1_i2) that has been found to be important in high-temperature adaptation of these dinoflagellates [19]. Upon programming the AI to output the conditions that would maximize the likelihood of a coral resisting bleaching based on the gen-reg-lasso model, _z_-scores for the activoxin and SMC protein of \(\sim\)2.2 and \(-\)1.3, respectively, were yielded; a coral maintaining abnormally high concentrations of the former and abnormally low concentrations of the latter would stand a 50% chance of being bleaching-resistant.