Table 1 Results from models tested in this work on Kaggle OpenVaccine public leaderboard, private test set and orthogonal mRNA degradation results
From: Deep learning models for predicting RNA degradation via dual crowdsourcing
Public test set (400 constructs, 27,200 nt) | Private test set (1,801 constructs, 162,316 nt) | mRNA degradation prediction from ref. 6 (188 constructs) | |
|---|---|---|---|
Metric | MCRMSE | MCRMSE | Spearman correlation |
Experimental error | 0.12491 | 0.10571 | 0.88a |
Single model (blind prediction) | |||
DegScore | 0.39219 | 0.47297 | 0.36 |
DegScore-XGBoost | 0.35854 | 0.43850 | 0.42 |
Nullrecurrent | 0.22758 | 0.34198 | 0.43 |
Kazuki2 | 0.22756 | 0.34266 | 0.48 |
Ensembled models (post hoc) | |||
Genetic algorithm (10 of top 100 selected) | 0.2237 | 0.3397 | |
Ensemble top two models | 0.2244 | 0.33788 | 0.47 |
Genetic algorithm on private test set | 0.3382 | – |
