Extended Data Fig. 5: Performance on individual data sets with NDCG.
From: Learning protein fitness models from evolutionary and assay-labeled data
Analogous to Fig. 4, but using NDCG. (a) Other than the EVmutation Potts model, the DeepSequence VAE, and Profile HMM, none of which use supervised data, all other methods here used 240 labeled training sequences. Each colored dot is the average NDCG from 20 random train-test splits. Random horizontal jitter was added for display purposes. The bottom row of black dots indicates the effective MSA size determined by accounting for sequence similarity with sample reweighting at 80% identity cutoff. (b) Summary of how often each modeling strategy had maximal NDCG. Such modelling strategies were determined by first identifying the top-performing strategy for any given scenario, and then also identifying any other strategy that came within the 95% confidence interval of the top performer.
