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Table 1 Evaluation of ONN4MST on all five datasets

From: Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches

Dataset No. biomes No. samples All features Selected features
Pr Rc Acc Fmax AUC Pr Rc Acc Fmax AUC
Combined 114 125,823 0.826 0.662 0.995 0.740 0.971 0.868 0.774 0.997 0.820 0.977
Human 25 53,553 0.822 0.521 0.984 0.695 0.972 0.894 0.826 0.991 0.863 0.984
Water 44 27,667 0.842 0.766 0.992 0.803 0.966 0.854 0.764 0.992 0.813 0.971
Soil 16 11,528 0.915 0.778 0.986 0.850 0.974 0.892 0.881 0.989 0.890 0.982
FEAST 3 10,270 0.793 0.795 0.984 0.803 0.980 0.895 0.812 0.989 0.862 0.991
  1. ONN4MST achieved the accuracy higher than 0.98 for all five datasets, and the AUC higher than 0.97 for all five datasets. For each dataset, we used the model trained on that dataset for evaluation. The evaluation procedure of the ONN model is described in the “Methods” section. ONN4MST based on all features and selected features were both evaluated at the bottom (sixth) layer with a threshold of 0.5
  2. Abbreviations: Pr precision, Rc recall, Acc accuracy