TREC 2005 Genomics Track Results

William Hersh, Track Chair
Last updated - February 15, 2006

This page contains the official results for the submissions to the TREC 2005 Genomics Track.  The following results are displayed:

Ad Hoc Task Run Descriptions

Tags for run and group with run type and system description provided by each group:

Run Tag Group Type
 System Description
asubaral arizonau.baral m (Not provided.)
CCP0 ucolorado.cohen m LocusLink gene synonym expansion; stemming; topic-specific keyword expansion; UMLS for disease synonyms, with heavy manual filtering of synonyms for "cancer"; weighted title over abstract.
CCP1 ucolorado.cohen m LocusLink gene synonym expansion, converting all synonyms to a "bag of words", weighting individual words by frequency in synset; stemming; topic-specific keyword expansion; UMLS for disease synonyms, also converted to BOW; weighted title over abstract.
dcu1 dublincityu.gurrin a This is the result of pseudo-relevance feedback on a baseline obtained with the DCU CDVP search engine Fisreal. Our search engine implements the BM25 probabilistic algorithm and the pseudo-relevance feedback is using Robertson Offer Weight method. The feedback aims at expanding the original queries with terms related to the generic structure of the queries, i.e. the Generic Topic Templates. The expansion terms were extracted from the sample search results using the relevance judgment provided.
dcu2 dublincityu.gurrin a This is the result of pseudo-relevance feedback on a baseline obtained with the DCU CDVP search engine Fisreal. Our search engine implements the BM25 probabilistic algorithm and the pseudo-relevance feedback is using Robertson Offer Weight method. The feedback aims at expanding the original queries with terms related to the generic structure of the queries, i.e. the Generic Topic Templates (GTTs). The top 5 documents for each topic of the same GTT are assumed relevant and GTT-related structure terms are extracted from these documents to expand topics that are instances of that particular GTT.
dpsearch1 datapark.zakharov m The DataparkSearch engine of upcoming 4.32 version has been used with  fast method and default options of relevancy calculation. 
dpsearch2 datapark.zakharov m The DataparkSearch engine of upcoming 4.32 version has been used with  full method and default options of relevancy calculation. 
DUTAdHoc1 dalianu.yang m Our ad hoc task retreival system mainly includes the following features gene synonym expansion,medical term expansion based on the Metathesaurus of UMLS Knowledge Sources provided by NLM,different scoring strategy on different parts of Medline record(Title,Abstract,RN,MH,etc.). 
DUTAdHoc2 dalianu.yang m Our ad hoc task retreival system mainly includes the following features gene synonym expansion,medical term expansion based on the Metathesaurus of UMLS Knowledge Sources provided by NLM,different scoring strategy on different parts of Medline record(Title,Abstract,RN,MH,etc.)and Pseudo-relevant feedback. 
genome1 csusm.guillen a We used the INDRI system developed by UMASS and CMU to create five indexes. Then we used the "runquery" option to retrieve documents using the five indexes. The topics were mapped to the INDRI format before retrieving the documents.
genome2 csusm.guillen a We used the INDRI system developed by UMASS and CMU to create five indexes. Then we used the "runquery" option including the feedback parameter with 100 documents to retrieve documents. The topics were mapped to the INDRI system format before running the queries.
i2r1 iir.yu a Automatic of Institute for Infocomm Research.
i2r2 iir.yu a The 2nd from Institute for Infocomm Research
iasl1 academia.sinica.tsai a Without using query expansion in Template 1. Without relevance feedback.
iasl2 academia.sinica.tsai a With query expansion in Template 1 and relevance feedback
ibmadz05bs ibm.zhang a Primary run.   Enhanced automatic relevance feedback. Synonyms from external resources. Queries enhanced by bi-grams.  
ibmadz05us ibm.zhang a Secondary run. Enhanced automatic relevance feedback.   Synonyms from external resources.  
iitprf011003 iit.urbain a Modivied pvn with 1 iteration RF.
MARYGEN1 umaryland.oard a InQuery with proximity operators for phrases identified using MetaMap and disease name expansion using MetaMap
NCBIMAN nlm.wilbur m Same as NCBITHQ for all queries except template 1 (100-109) which used manual theme generation. 
NCBITHQ nlm.wilbur a Phrases and their variants are extracted from each query and used to form boolean queries. The non-gene resuls are expanded using a "theme" approach (naive Bayes scoring) to rescore the results. Document scores from individual queries are converted to probabilites and combined with fuzzy logic operations. Template 1 uses the MEDLINE nearest neighbor function instead of boolean queries on separate phrases, and combines the results with a generic "protocol theme".
NLMfusionA nlm-umd.aronson a combination of four systems  NCBI, Smart, InQuery, EZIR with query expansion 
NLMfusionB nlm-umd.aronson a combination by template of four systems  NCBI, Smart, InQuery, EZIR with query expansion 
NTUgah1 ntu.chen a The Entrez Gene and MeSH databases are used to identify important topic terms and their synonymns. For a topic, documents are first ranked by whether they contain all the important terms, and than by BM25 scores.
NTUgah2 ntu.chen a Same as NTUgah1, except that documents which contain all important terms in their abstracts or titles are ranked higher than those which contain important terms appearing only in their MeSH fields. 
OHSUall ohsu.hersh a Use all words in the narrative topic files. Zettair engine with Okapi k1=0.2
OHSUkey ohsu.hersh a Use only keywords in the narrative topic files. Zettair engine with Okapi k1=0.2
PDnoSE upadova.bacchin a This is a TF-IDF vector based IR system.
PDSESe02 upadova.bacchin a The IR system uses a query expansion technique based on symbol recognition.
SFUshi simon-fraseru.shi m 1. Make use of public gene/protein database to expand query; 2. Use structured query to express logic relations among query terms; 3. Use pseudo relevance feedback; 
THUIRgen1S tsinghua.ma a Structural Query Language; UniSentence, BiSentence and Multi-field Retrieval; Internal Resource Utility; Iterative Result Fusion; Stemming, Stopword, BM2500, Pseudo-relevance feedback; (More details will be involved in our report.) 
THUIRgen2P tsinghua.ma a Pattern Generation; Pattern Matching and Scoring; Prefix, Midfix, and Suffix for Given Template Expansion; Balance Between Precision and Recall; Internal Resource Utility. (More details will be involved in our report.)
tnog10 tno.erasmus.kraaij a JM smoothed language model
tnog10p tno.erasmus.kraaij a JM smoothed language model, + Journal title prior
UAmscombGeFb uamsterdam.aidteam a that combines MeSH-heading based blind feedback with gene name synonym and acronym expansion
UAmscombGeMl uamsterdam.aidteam a that combines gene name synonym and acronym expansion with automatic MeSH-heading lookup procedure
UBIgeneA suny-buffalo.ruiz a Automatic using gene expansion with MeSH terms, minimal stemming, and restricted word bigrams. IR system  SMART, weighting scheme atn.ann
UICgen1 uillinois-chicago.liu a Porter stemming; OKAPI; Query expansion; Weighting scheme
UIowa05GN101 uiowa.eichmann a Precision focussed run.  Uses a more stringent threshold on the ranked results.
UIowa05GN102 uiowa.eichmann a Recall focussed run.
UIUCgAuto uiuc.zhai a This is produced completely automatically from the original topic description. It performs pseudo feedback based on the structure of the query using a language modeling approach. 
UIUCgInt uiuc.zhai i This is produced with human relevance judgments on the top 20 documents from the initial retrieval run. It also uses biology resources to automatically expand the original queries.   
UMD01 umichigan-dearborn.murphey a We extracted key words from each topic and combine keywords with logic connection AND or OR. We then calculated similarity scores of all documents with this combination and sorted the results.
UMD02 umichigan-dearborn.murphey a We extracted key words from each topic,then calculated similarity scores by Okapi BM25 method and sorted the results.
UniGe2 u.geneva a This runs merges two different lists  1) a with query expansion, based on gene and protein names and rocchio; 2) a with expansion based on MeSH terms. Warning  this is intended to replace the 'UniGeC' ! Please, 'UniGeC' is corrupted and should be deleted.
UniGeNe u.geneva a Data fusion (combination of two result lists) by  a) a probabilistic model + pseudo-relance feedback (10 docs / 20 terms)  b) same probabilistic model with modified queries (with thesaurus of gene and protein names) + PRF (10 docs / 20 terms)
UniNeHug2 uneuchatel.savoy a Probabilistic model + pseudo-relance feedback (10docs / 20 terms)
UniNeHug2c uneuchatel.savoy a Data fusion (combination of two result lists) by  a) a probabilistic model + pseudo-relance feedback (10docs / 20 terms) b) same probabilistic model with modified queries (with genomics DB) + PRF (10 docs / 20 terms)
uta05a utampere.pirkola a This is a simple that serves as a baseline for our second run. Topic keys were used in queries, no expansion etc. was used. Different columns of a template were linked by a Boolean conjunction.
uta05i utampere.pirkola i 1. Synonymous gene names for the topic gene names were retrieved from the Entrez Gene. 2. Our automatic queries (uta05a) were expanded with the synonyms. 3. The expanded queries were on the test database. 4. Final queries (Boolean queries) were formulated by further expanding the queries with MH terms and synonyms found in the top documents of the initial search.
uwmtEg05 uwaterloo.clarke a Plain Okapi BM25 run, with stemming applied to all terms that do not contain numerical characters.
uwmtEg05fb uwaterloo.clarke a Okapi BM25 with standard Okapi feedback; stemming applied to all terms that do not contain numerical characters. This is a two-stage run, using the top 40 documents returned by the first stage to add pseudo-relevance feedback terms to the query in the second stage.
wim1 fudan.niu a language model,greek letter,query expansion
wim2 fudan.niu a gene noun,okapi,query expansion
YAMAHASHI1 utokyo.takahashi m Using MeSH for ranking.
YAMAHASHI2 utokyo.takahashi m Not Using MeSH for ranking.
york05ga1 yorku.huang a 1. use Okapi BM25 System with stuctured query function 2. use rules to expand the terms. 3. use BioNLP utility to identify the long form and acronym pairs. 4. use some rules to rebalance the weight for query term. 5. blank feedback with special term selection technique
york05gm1 yorku.huang m 1. use Okapi BM25 system with structured query function 2. use Acromed and LocusLink database to expand the terms. 3. mannualy select good quality expanded terms 4. use some rules to rebalance the weight for query term 5. blank feedback with special term selection technique

Ad Hoc Task Topic Statistics

Relevance judgment statistics for each topic:

Topic Pool Definitely
Relevant		 Possibly
Relevant		 Not
Relevant		 TREC
Relevant		 % relevant
100 704 22 52 630 74 10.5%
101 651 2 18 631 20 3.1%
102 1164 5 5 1154 10 0.9%
103 701 6 19 676 25 3.6%
104 629 0 4 625 4 0.6%
105 1133 4 85 1044 89 7.9%
106 1230 44 125 1061 169 13.7%
107 484 76 114 294 190 39.3%
108 1092 76 127 889 203 18.6%
109 389 165 14 210 179 46.0%
110 934 4 12 918 16 1.7%
111 675 109 93 473 202 29.9%
112 870 4 7 859 11 1.3%
113 1356 10 4 1342 14 1.0%
114 754 210 169 375 379 50.3%
115 1350 3 12 1335 15 1.1%
116 1265 58 28 1179 86 6.8%
117 1094 527 182 385 709 64.8%
118 937 20 12 905 32 3.4%
119 589 42 19 528 61 10.4%
120 527 223 122 182 345 65.5%
121 422 17 25 380 42 10.0%
122 871 19 37 815 56 6.4%
123 1029 5 32 992 37 3.6%
124 752 8 53 691 61 8.1%
125 1202 3 8 1191 11 0.9%
126 1320 190 117 1013 307 23.3%
127 841 1 3 837 4 0.5%
128 954 21 53 880 74 7.8%
129 987 16 22 949 38 3.9%
130 813 9 23 781 32 3.9%
131 431 2 40 389 42 9.7%
132 531 3 27 501 30 5.6%
133 523 0 5 518 5 1.0%
134 732 2 9 721 11 1.5%
136 853 0 3 850 3 0.4%
137 1129 12 39 1078 51 4.5%
138 501 6 6 489 12 2.4%
139 380 15 20 345 35 9.2%
140 395 14 15 366 29 7.3%
141 520 34 47 439 81 15.6%
142 528 151 120 257 271 51.3%
143 902 0 4 898 4 0.4%
144 1212 1 1 1210 2 0.2%
145 288 10 22 256 32 11.1%
146 825 370 67 388 437 53.0%
147 659 0 10 649 10 1.5%
148 536 0 11 525 11 2.1%
149 1294 6 17 1271 23 1.8%

Ad Hoc Task Results by Run

Results for each run with major evaluation statistics, sorted by MAP:

Run Type MAP R-Prec B-pref P10 P100 P1000
york05gm1 m 0.302 0.3212 0.3155 0.4551 0.2543 0.0748
york05ga1 a 0.2888 0.3118 0.3061 0.4592 0.2557 0.0721
ibmadz05us a 0.2883 0.3091 0.3026 0.4735 0.2643 0.0766
ibmadz05bs a 0.2859 0.3061 0.2987 0.4694 0.2606 0.0761
uwmtEg05 a 0.258 0.2853 0.2781 0.4143 0.2292 0.0718
UIUCgAuto a 0.2577 0.2688 0.2708 0.4122 0.231 0.0709
UIUCgInt i 0.2487 0.2627 0.267 0.4224 0.2355 0.0694
NLMfusionA a 0.2479 0.2767 0.2675 0.402 0.2378 0.0688
iasl1 a 0.2453 0.2708 0.265 0.398 0.2292 0.0698
NLMfusionB a 0.2453 0.2666 0.2541 0.4082 0.2339 0.0693
UniNeHug2 a 0.2439 0.2582 0.264 0.398 0.2308 0.0712
UniGe2 a 0.2396 0.2705 0.2608 0.3878 0.2361 0.0711
i2r1 a 0.2391 0.2629 0.2716 0.3898 0.231 0.0668
uta05a a 0.2385 0.2638 0.2546 0.4163 0.2255 0.0678
i2r2 a 0.2375 0.2622 0.272 0.3878 0.2296 0.067
UniNeHug2c a 0.2375 0.2662 0.2589 0.3878 0.239 0.0725
uwmtEg05fb a 0.2359 0.2573 0.2552 0.3878 0.2257 0.0712
DUTAdHoc2 m 0.2349 0.2678 0.2725 0.3939 0.2206 0.0648
THUIRgen1S a 0.2349 0.2663 0.2568 0.4224 0.2214 0.0622
tnog10 a 0.2346 0.2607 0.2564 0.3857 0.2227 0.0668
DUTAdHoc1 m 0.2344 0.2718 0.2726 0.402 0.22 0.0645
tnog10p a 0.2332 0.2506 0.2555 0.402 0.2173 0.0668
iasl2 a 0.2315 0.2465 0.2487 0.3816 0.2276 0.07
UAmscombGeFb a 0.2314 0.2638 0.2592 0.4163 0.2271 0.0612
UBIgeneA a 0.2262 0.2567 0.2542 0.3633 0.2122 0.0683
OHSUkey a 0.2233 0.2569 0.2544 0.3735 0.2169 0.0632
NTUgah2 a 0.2204 0.2562 0.2498 0.398 0.1996 0.0644
THUIRgen2P a 0.2177 0.2519 0.2395 0.4143 0.2198 0.0695
NTUgah1 a 0.2173 0.2558 0.2513 0.3918 0.1998 0.0615
UniGeNe a 0.215 0.2364 0.2347 0.3367 0.2237 0.0694
UAmscombGeMl a 0.2015 0.2325 0.232 0.3551 0.2094 0.0568
uta05i i 0.198 0.2411 0.229 0.4082 0.2137 0.0547
PDnoSE a 0.1937 0.2213 0.2183 0.3571 0.2006 0.063
iitprf011003 a 0.1913 0.2142 0.2205 0.3612 0.2018 0.065
dcu1 a 0.1851 0.2178 0.2129 0.3816 0.1851 0.0577
dcu2 a 0.1844 0.2234 0.214 0.3959 0.1896 0.0599
SFUshi m 0.1834 0.2072 0.2149 0.3429 0.1898 0.0608
OHSUall a 0.183 0.2285 0.2221 0.3286 0.1965 0.0592
wim2 a 0.1807 0.2006 0.2055 0.3 0.1794 0.057
genome1 a 0.1803 0.2174 0.211 0.3245 0.1749 0.0577
wim1 a 0.1781 0.2094 0.2076 0.3347 0.181 0.0592
NCBITHQ a 0.1777 0.214 0.2192 0.3041 0.1824 0.0526
NCBIMAN m 0.1747 0.2081 0.2181 0.3122 0.182 0.0519
UICgen1 a 0.1738 0.2079 0.2046 0.3082 0.1941 0.0579
MARYGEN1 a 0.1729 0.1954 0.1898 0.3041 0.1439 0.0409
PDSESe02 a 0.1646 0.1928 0.1928 0.3224 0.1904 0.0615
genome2 a 0.1642 0.1931 0.1928 0.298 0.1676 0.0565
UIowa05GN102 a 0.1303 0.1861 0.1693 0.2898 0.1671 0.0396
UMD01 a 0.1221 0.1541 0.1435 0.3224 0.1473 0.0321
UIowa05GN101 a 0.1095 0.1636 0.1414 0.2857 0.1571 0.026
CCP0 m 0.1078 0.1486 0.1311 0.2837 0.1439 0.0203
YAMAHASHI2 m 0.1022 0.1236 0.1276 0.2653 0.1312 0.0369
YAMAHASHI1 m 0.1003 0.1224 0.1248 0.2531 0.1267 0.0356
dpsearch2 m 0.0861 0.1169 0.1034 0.2633 0.1231 0.0278
dpsearch1 m 0.0827 0.1177 0.1017 0.2551 0.1182 0.0274
asubaral m 0.0797 0.1079 0.0967 0.2714 0.1061 0.0142
CCP1 m 0.0554 0.0963 0.0775 0.1878 0.0951 0.0134
UMD02 a 0.0544 0.0703 0.0735 0.1755 0.0843 0.0166

MAP with 95% confidence interval for all runs:

errbar
All statistics for each run, sorted by MAP:
runs

Ad Hoc Task Results by Topic

Major evaluation statistics for each topic:

Topic MAP R-Prec B-Pref P10 P100 P1000
100 0.1691 0.2148 0.1616 0.3569 0.1916 0.0550
101 0.0454 0.0526 0.0285 0.0483 0.0516 0.0141
102 0.0110 0.0172 0.0100 0.0172 0.0091 0.0036
103 0.0603 0.0945 0.0570 0.0948 0.0602 0.0169
104 0.0694 0.0948 0.0582 0.0690 0.0124 0.0023
105 0.1102 0.1703 0.1461 0.4655 0.1586 0.0327
106 0.0625 0.1120 0.1231 0.3138 0.1433 0.0491
107 0.4184 0.4297 0.5289 0.9103 0.5934 0.1373
108 0.1224 0.1973 0.2206 0.4828 0.2788 0.0695
109 0.5347 0.5196 0.6512 0.9190 0.7066 0.1345
110 0.0137 0.0248 0.0154 0.0224 0.0128 0.0055
111 0.2192 0.2985 0.2926 0.3569 0.3140 0.1170
112 0.2508 0.3354 0.2754 0.3586 0.0481 0.0062
113 0.3124 0.3498 0.3164 0.3931 0.0822 0.0096
114 0.3876 0.4364 0.5505 0.8259 0.6697 0.2476
115 0.0378 0.0437 0.0340 0.0534 0.0193 0.0036
116 0.1103 0.1720 0.1456 0.2879 0.1636 0.0359
117 0.3796 0.4739 0.5126 0.8345 0.7409 0.4099
118 0.1343 0.1460 0.1369 0.3276 0.0634 0.0145
119 0.5140 0.5212 0.5075 0.8190 0.3462 0.0493
120 0.5769 0.5421 0.7217 0.9259 0.8091 0.2695
121 0.6205 0.6560 0.6394 0.7983 0.3040 0.0337
122 0.1423 0.2023 0.1590 0.3569 0.1510 0.0320
123 0.0375 0.0708 0.0474 0.1121 0.0493 0.0133
124 0.1519 0.2035 0.1693 0.5103 0.1505 0.0324
125 0.0772 0.0862 0.0708 0.0897 0.0209 0.0028
126 0.1313 0.2172 0.2388 0.3966 0.2979 0.1422
127 0.1015 0.1250 0.0862 0.0759 0.0155 0.0028
128 0.0921 0.1424 0.1062 0.3224 0.1247 0.0366
129 0.0864 0.1393 0.0939 0.1793 0.0984 0.0212
130 0.3390 0.3545 0.3346 0.6362 0.1388 0.0194
131 0.4436 0.4384 0.4230 0.5517 0.2790 0.0343
132 0.1048 0.1558 0.1115 0.2431 0.0966 0.0196
133 0.0328 0.0207 0.0172 0.0172 0.0140 0.0029
134 0.1687 0.1771 0.1582 0.1914 0.0364 0.0069
136 0.0032 0.0000 0.0000 0.0000 0.0019 0.0010
137 0.0676 0.1146 0.0767 0.1776 0.0848 0.0232
138 0.2196 0.2342 0.2029 0.2534 0.0552 0.0089
139 0.3600 0.3941 0.3488 0.5810 0.2052 0.0305
140 0.2700 0.3115 0.2423 0.3810 0.1843 0.0248
141 0.2381 0.2735 0.2053 0.3362 0.2598 0.0699
142 0.4416 0.4608 0.5911 0.8569 0.6409 0.2098
143 0.0031 0.0043 0.0011 0.0034 0.0021 0.0009
144 0.0734 0.0603 0.0431 0.0276 0.0053 0.0009
145 0.3363 0.3761 0.3238 0.5931 0.1852 0.0260
146 0.4808 0.4961 0.6325 0.8466 0.7212 0.3076
147 0.0087 0.0138 0.0057 0.0138 0.0091 0.0040
148 0.0411 0.0376 0.0144 0.0293 0.0407 0.0066
149 0.0286 0.0495 0.0304 0.0603 0.0347 0.0089

Plot of number of relevant and MAP for each topic:

topics

Ad Hoc Task Results by GTT

Relevance judgment statistics for each GTT:

Topics Pool Definitely
Relevant		 Possibly
Relevant		 Not
Relevant		 TREC
Relevant		 % relevant
100-109 817.7 40.0 56.3 721.4 96.3 14.4%
110-119 982.4 98.7 53.8 829.9 152.5 17.1%
120-129 890.5 50.3 47.2 793.0 97.5 13.0%
130-139 654.8 5.4 19.1 630.2 24.6 4.3%
140-149 715.9 58.6 31.4 625.9 90.0 14.4%

Major evaluation statistics for each GTT:

Topics
 MAP R-Prec B-Pref P10 P100 P1000
100-109 0.1603 0.1903 0.1985 0.3678 0.2206 0.0515
110-119 0.2360 0.2802 0.2787 0.4279 0.2460 0.0899
120-129 0.2018 0.2385 0.2333 0.3767 0.2021 0.0587
130-139 0.1932 0.2099 0.1859 0.2946 0.1013 0.0163
140-149 0.1922 0.2084 0.2090 0.3148 0.2083 0.0659

Duplicate analysis

Topics 104, 116, 117, 133, 134, 135, 141, 144, 148, and 149 were judged in duplicate.  The following table shows the overlap in judgments across judges:


 Duplicate - Relelvant Duplicate -
Not relevant		 Total
Original - Relevant 1100 629 1729
Original -
Not relevant		 546 8204 8750
Total
 1646 8833 10479

The Cohen's kappa statistic for these judgments was 0.585.

The following table compares MAP with the official judgments, an AND of the original and duplicate judgments, and an OR of the original and duplicate judgments:

Run MAP AND OR
york05gm1 0.302 0.3221 0.3257
york05ga1 0.2888 0.3081 0.3099
ibmadz05us 0.2883 0.3054 0.3121
ibmadz05bs 0.2859 0.3087 0.3141
uwmtEg05 0.258 0.2767 0.2778
UIUCgAuto 0.2577 0.2765 0.2795
UIUCgInt 0.2487 0.2656 0.2675
NLMfusionA 0.2479 0.2701 0.2709
iasl1 0.2453 0.2666 0.2678
NLMfusionB 0.2453 0.2711 0.272
UniNeHug2 0.2439 0.261 0.2643
UniGe2 0.2396 0.2655 0.2646
i2r1 0.2391 0.2614 0.2615
uta05a 0.2385 0.2557 0.2565
i2r2 0.2375 0.254 0.2534
UniNeHug2c 0.2375 0.2621 0.2632
uwmtEg05fb 0.2359 0.2601 0.2638
DUTAdHoc2 0.2349 0.2546 0.2525
THUIRgen1S 0.2349 0.2514 0.2522
tnog10 0.2346 0.2552 0.2559
DUTAdHoc1 0.2344 0.2533 0.2511
tnog10p 0.2332 0.2532 0.254
iasl2 0.2315 0.2499 0.2535
UAmscombGeFb 0.2314 0.2577 0.2569
UBIgeneA 0.2262 0.2452 0.2467
OHSUkey 0.2233 0.2474 0.2478
NTUgah2 0.2204 0.2465 0.2441
THUIRgen2P 0.2177 0.2355 0.2372
NTUgah1 0.2173 0.2385 0.2361
UniGeNe 0.215 0.2362 0.2386
UAmscombGeMl 0.2015 0.2233 0.2231
uta05i 0.198 0.2135 0.2148
PDnoSE 0.1937 0.2095 0.2103
iitprf011003 0.1913 0.2128 0.2113
dcu1 0.1851 0.203 0.2019
dcu2 0.1844 0.2007 0.2001
SFUshi 0.1834 0.191 0.1943
OHSUall 0.183 0.2002 0.2014
wim2