16th Community Wide Experiment on the
Critical Assessment of Techniques for Protein Structure Prediction
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RNA Predictions Analysis : Group performance based on combined z-scores
Results Home Table Browser
  • Analysis on the models designated as "1"
  • Analysis on the models with the best scores
The final ranking was generated according to the formula: 0.3*Z-score(TM) + 0.3*Z-score(GDTTS) + 0.4*Z-score(LDDT).
    #     GR
    name		     GR
    code		     Targets Count     SUM Zscore
    (>0.0)		     Rank SUM Zscore
    (>0.0)		     AVG Zscore
    (>0.0)		     Rank AVG Zscore
    (>0.0)
1 Vfold 481 32 32.0297 1 1.0009 1
2 GuangzhouRNA-human 183 32 23.1311 2 0.7228 4
3 KiharaLab 294 33 21.7207 3 0.6582 5
4 Yang-Server 052 31 19.2259 4 0.6202 6
5 GeneSilico 338 33 18.7057 5 0.5668 7
6 RNAFOLDX 435 31 15.9638 6 0.5150 12
7 elofsson 241 33 15.5576 7 0.4714 18
8 CSSB_experimental 286 33 14.6651 8 0.4444 21
9 GuangzhouRNA-meta 417 32 14.3026 9 0.4470 19
10 BRIQX 238 26 14.0567 10 0.5406 8
11 AF3-server 304 33 13.8772 11 0.4205 25
12 Diff 033 31 13.7837 12 0.4446 20
13 LCBio 189 26 12.6123 13 0.4851 16
14 406 159 24 12.5505 14 0.5229 11
15 RNApolis 063 26 12.2701 15 0.4719 17
16 CoDock 262 24 12.0052 16 0.5002 13
17 GromihaLab 272 28 11.9564 17 0.4270 23
18 Bhattacharya 369 30 11.5918 18 0.3864 26
19 405 325 22 10.7758 19 0.4898 14
20 falcon2 208 31 10.6685 20 0.3441 29
21 OpenComplex 167 32 9.7923 21 0.3060 32
22 NKRNA-s 028 33 9.6485 22 0.2924 35
23 isyslab-hust 235 32 9.4140 23 0.2942 34
24 Zheng 462 33 9.1183 24 0.2763 37
25 MIEnsembles-Server 110 33 9.0121 25 0.2731 38
26 Yang-Multimer 456 23 8.6684 26 0.3769 28
27 B-LAB 231 31 8.1962 27 0.2644 40
28 OpenComplex_Server 450 31 7.8551 28 0.2534 43
29 dNAfold 448 26 7.7502 29 0.2981 33
30 GuangzhouRNA_AI 317 32 5.5492 30 0.1734 50
31 dfr 165 20 5.0696 31 0.2535 42
32 RNA_Dojo 006 23 4.7838 32 0.2080 45
33 LCDD-team 055 5 4.1871 33 0.8374 3
34 nfRNA 307 16 4.0565 34 0.2535 41
35 Huang-HUST 091 13 3.6251 35 0.2789 36
36 dMNAfold 143 11 3.3727 36 0.3066 31
37 SNU-CHEM-lig 408 5 2.6949 37 0.5390 9
38 comppharmunibas 020 3 2.5207 38 0.8402 2
39 SoutheRNA 156 32 2.4369 39 0.0762 55
40 kiharalab_server 267 27 2.1399 40 0.0793 54
41 ShanghaiTech-server 423 9 1.8683 41 0.2076 46
42 ShanghaiTech-human 298 9 1.8683 42 0.2076 47
43 SimRNA-server 094 24 1.6968 43 0.0707 56
44 mmagnus 403 4 1.6930 44 0.4232 24
45 FrederickFolding 276 3 1.4678 45 0.4893 15
46 OmniFold 400 4 1.0714 46 0.2679 39
47 MULTICOM_ligand 207 5 0.7603 47 0.1521 51
48 test001 202 1 0.5357 48 0.5357 10
49 MULTICOM 051 2 0.4608 49 0.2304 44
50 GeneSilicoRNA-server 306 23 0.4584 50 0.0199 58
51 KUMC 227 5 0.4332 51 0.0866 53
52 Pcons 471 1 0.4313 52 0.4313 22
53 Zheng-Multimer 147 1 0.3848 53 0.3848 27
54 Dokholyan 439 8 0.3684 54 0.0460 57
55 MULTICOM_AI 331 1 0.3192 55 0.3192 30
56 MULTICOM_LLM 319 1 0.1930 56 0.1930 48
57 MULTICOM_GATE 425 1 0.1930 56 0.1930 48
58 MULTICOM_human 345 2 0.1930 56 0.0965 52
59 PerezLab_Gators 358 13 0.0951 59 0.0073 59
60 UDMod 447 2 0.0045 60 0.0022 60
61 thermomaps 169 24 0.0000 61 0.0000 61
62 UNRES 261 18 0.0000 61 0.0000 61
63 PocketTracer 464 5 0.0000 61 0.0000 61
64 SwRI 026 1 0.0000 61 0.0000 61
65 AIR 367 1 0.0000 61 0.0000 61
D1273  R1203  R1205  R1209  R1211  R1212  R1221s2  R1221s3  R1224s2  R1224s3  R1241  R1242  R1248  R1250  R1251  R1252  R1253v1  R1253v2  R1254  R1255  R1256  R1261  R1262  R1263  R1264  R1271  R1281  R1283v1  R1285  R1286  R1288  R1289  R1290  R1291  R1293  R1296 
The cummulative z-scores in this table are calculated according to the following procedure (example for the "first" models):
1. Calculate zscores from the raw scores for all "first" models (corresponding values from the main result table);
2. Remove outliers - models with zscores below the tolerance threshold (set to -2.0);
3. Recalculate zscores on the reduced dataset;
4. Assign z-scores below the penalty threshold (either -2.0 or 0.0) to the value of this threshold.
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