Fig. 3: The performance of GeoPoc and other models on protein optimal temperature prediction. | Communications Biology

Fig. 3: The performance of GeoPoc and other models on protein optimal temperature prediction.

From: Accurately predicting optimal conditions for microorganism proteins through geometric graph learning and language model

Fig. 3

The regression correlation between prediction and ground truth values of optimal temperature for all proteins in 5-fold CV (a) and test (b). For the color bar in the (a)/(b), we used a density-based color scale computed via Gaussian kde, and the square root transformation was applied to enhance visual clarity in regions with high data density. The MAE for the 5-fold CV is 6.402 ± 0.116 (a), while the MAE for the test set is 6.083 (b). c Performance comparison between GeoPoc and ablation methods in the test set using PCC. d Receiver Operating Characteristic curves of GeoPoc and comparison methods on the independent test set. e Comparison of GeoPoc and comparison methods on threshold-dependent metrics. f The regression correlation between ground truth and predicted values of optimal temperature (°C) for all species in the test set. Note: w/o denotes without. Source data are provided as a Source Data file.

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