Update README.md

Author: JianLiu2025

README.md

@@ -175,17 +175,17 @@ model2,0.67,0.71, ...
 
 Example of a model quality prediction file (./Predictions/CASP16_inhouse_TOP5_dataset/H1202.csv):
 ```
-model,PSS,DProQA,VoroIF-GNN-score,VoroIF-GNN-pCAD-score,VoroMQA-dark,GCPNet-EMA,GATE-AFM
-deepmsa2_14_ranked_2.pdb,0.9545535989717224,0.02895,0.0,0.0,0.0,0.7771772742271423,0.5923953714036315
-afsample_v2_ranked_2.pdb,0.8873916966580978,0.0066,0.0,0.0,0.0,0.7705466747283936,0.575105558750621
-def_mul_tmsearch_ranked_0.pdb,0.9609340102827764,0.02353,0.0,0.0,0.0,0.7641939520835876,0.5981529354257233
-deepmsa2_1_ranked_4.pdb,0.96272264781491,0.02055,0.0,0.0,0.0,0.7685595154762268,0.5959772306691834
-deepmsa2_1_ranked_2.pdb,0.9606568380462726,0.02318,0.0,0.0,0.0,0.7671180963516235,0.5983494717414063
-afsample_v2_r21_not_ranked_1.pdb,0.9234104884318768,0.0192,0.0,0.0,0.0,0.7699458599090576,0.5879402631363266
-deepmsa2_0_ranked_3.pdb,0.9607991259640104,0.02123,0.0,0.0,0.0,0.7682469487190247,0.5953465198918304
-afsample_v1_r21_not_ranked_1.pdb,0.9156177377892032,0.02246,0.0,0.0,0.0,0.7822033762931824,0.5772502685580536
-folds_iter_esm_1_ranked_1.pdb,0.9471744215938304,0.01475,0.0,0.0,0.0,0.7621756196022034,0.5904867273330673
-deepmsa2_15_ranked_3.pdb,0.956274524421594,0.02606,0.0,0.0,0.0,0.7756944894790649,0.5937158219111754
+model,PSS,DProQA,VoroIF-GNN-score,VoroIF-GNN-pCAD-score,VoroMQA-dark,GCPNet-EMA,GATE-AFM,AFM-Confidence
+deepmsa2_14_ranked_2.pdb,0.9545535989717224,0.02895,0.0,0.0,0.0,0.7771772742271423,0.5923953714036315,0.8254922444800168
+afsample_v2_ranked_2.pdb,0.8873916966580978,0.0066,0.0,0.0,0.0,0.7705466747283936,0.575105558750621,0.8153403780624796
+def_mul_tmsearch_ranked_0.pdb,0.9609340102827764,0.02353,0.0,0.0,0.0,0.7641939520835876,0.5981529354257233,0.8133504286051549
+deepmsa2_1_ranked_4.pdb,0.96272264781491,0.02055,0.0,0.0,0.0,0.7685595154762268,0.5959772306691834,0.8178802534659162
+deepmsa2_1_ranked_2.pdb,0.9606568380462726,0.02318,0.0,0.0,0.0,0.7671180963516235,0.5983494717414063,0.8183128442689481
+afsample_v2_r21_not_ranked_1.pdb,0.9234104884318768,0.0192,0.0,0.0,0.0,0.7699458599090576,0.5879402631363266,0.8204161898081545
+deepmsa2_0_ranked_3.pdb,0.9607991259640104,0.02123,0.0,0.0,0.0,0.7682469487190247,0.5953465198918304,0.8183400300533047
+afsample_v1_r21_not_ranked_1.pdb,0.9156177377892032,0.02246,0.0,0.0,0.0,0.7822033762931824,0.5772502685580536,0.8226690041151985
+folds_iter_esm_1_ranked_1.pdb,0.9471744215938304,0.01475,0.0,0.0,0.0,0.7621756196022034,0.5904867273330673,0.8215535911325099
+deepmsa2_15_ranked_3.pdb,0.956274524421594,0.02606,0.0,0.0,0.0,0.7756944894790649,0.5937158219111754,0.8243908296207267
 ```
 
 ### Output:
@@ -196,6 +196,12 @@ The script generates a CSV file summarizing the evaluation results. Each row cor
 - *_loss: The difference between the quality score of the truely best model of a target and that of the top-ranked model selected by the predicted quality scores.
 - *_auroc: AUROC from ROC analysis, measuring how well the EMA method distinguishes high-quality models (top 25%) from others.
 
+Example of a output file:
+
+```
+target,PSS_pearson,PSS_spearman,PSS_loss,PSS_auroc,DProQA_pearson,DProQA_spearman,DProQA_loss,DProQA_auroc,VoroIF-GNN-score_pearson,VoroIF-GNN-score_spearman,VoroIF-GNN-score_loss,VoroIF-GNN-score_auroc,VoroIF-GNN-pCAD-score_pearson,VoroIF-GNN-pCAD-score_spearman,VoroIF-GNN-pCAD-score_loss,VoroIF-GNN-pCAD-score_auroc,VoroMQA-dark_pearson,VoroMQA-dark_spearman,VoroMQA-dark_loss,VoroMQA-dark_auroc,GCPNet-EMA_pearson,GCPNet-EMA_spearman,GCPNet-EMA_loss,GCPNet-EMA_auroc,GATE-AFM_pearson,GATE-AFM_spearman,GATE-AFM_loss,GATE-AFM_auroc,AFM-Confidence_pearson,AFM-Confidence_spearman,AFM-Confidence_loss,AFM-Confidence_auroc
+H1202,0.0648102729743337,0.1695381876334342,0.028000000000000025,0.5,0.11790990707180772,-0.13413140758032455,0.0050000000000000044,0.5,-0.020603323167442494,-0.031220710744368295,0.016000000000000014,0.5095527954681397,-0.028282504057759977,-0.03428508620550331,0.016000000000000014,0.509130572464023,-0.028878249628187847,-0.034223373004110276,0.05900000000000005,0.5091129798388515,0.11945280299656955,0.04279138121740533,0.040000000000000036,0.5,-0.02194819533685046,0.10417440398523113,0.025000000000000022,0.5,-0.003912594407666805,-0.040927588302773835,0.040000000000000036,0.5
+```
 
 ## III. Reproducing the evaluation results of GATE and other baseline EMA methods in PSBench