Datasets:
Add files using upload-large-folder tool
Browse filesThis view is limited to 50 files because it contains too many changes.
See raw diff
- mendelian_traits_matched_9/AUPRC_by_chrom/3_prime_UTR_variant/GPN_final.LogisticRegression.chrom.subset_from_all.csv +8 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/3_prime_UTR_variant/GPN_final_LLR.minus.score.csv +8 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/5_prime_UTR_variant/GPN_final.LogisticRegression.chrom.subset_from_all.csv +18 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/5_prime_UTR_variant/GPN_final_LLR.minus.score.csv +18 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/all/Borzoi_L2_L2.plus.all.csv +20 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/all/CADD+GPN-MSA+Borzoi.LogisticRegression.chrom.csv +20 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/all/CADD.LogisticRegression.chrom.csv +20 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/all/Caduceus_InnerProduct.minus.score.csv +20 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/all/Enformer_L2_L2.plus.all.csv +20 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/all/GPN-MSA+Borzoi.LogisticRegression.chrom.csv +20 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/all/GPN_final_Embeddings.plus.cosine_distance.csv +20 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/all/GPN_final_EuclideanDistance.plus.score.csv +20 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/all/NucleotideTransformer_Embeddings.plus.cosine_distance.csv +20 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/non_coding_transcript_exon_variant/GPN_final_LLR.minus.score.csv +9 -0
- mendelian_traits_matched_9/AUPRC_by_chrom/nonexonic_AND_proximal/GPN_final_LLR.minus.score.csv +16 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/3_prime_UTR_variant/GPN_final.LogisticRegression.chrom.subset_from_all.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/3_prime_UTR_variant/GPN_final_LLR.minus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Borzoi.LogisticRegression.chrom.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Borzoi_L2_L2.plus.all.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/CADD+Borzoi.LogisticRegression.chrom.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/CADD+GPN-MSA+Borzoi.LogisticRegression.chrom.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/CADD.LogisticRegression.chrom.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/CADD.plus.RawScore.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Caduceus_Embeddings.minus.inner_product.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Caduceus_Embeddings.plus.cosine_distance.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Enformer.LogisticRegression.chrom.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA+Borzoi.LogisticRegression.chrom.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_Embeddings.minus.inner_product.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_Embeddings.plus.cosine_distance.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_Embeddings.plus.euclidean_distance.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_InnerProduct.minus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_LLR.minus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_absLLR.plus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final.LogisticRegression.chrom.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final_Embeddings.plus.cosine_distance.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final_InnerProduct.minus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final_LLR.minus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final_absLLR.plus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA.LogisticRegression.chrom.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_Embeddings.minus.inner_product.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_Embeddings.plus.cosine_distance.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_Embeddings.plus.euclidean_distance.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_InnerProduct.minus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_LLR.minus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/NucleotideTransformer_Embeddings.minus.inner_product.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/NucleotideTransformer_Embeddings.plus.cosine_distance.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/NucleotideTransformer_InnerProduct.minus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/NucleotideTransformer_absLLR.plus.score.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/no_cadd_overlap/Borzoi_L2_L2.plus.all.csv +2 -0
- mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/no_cadd_overlap/CADD.LogisticRegression.chrom.subset_from_all.csv +2 -0
mendelian_traits_matched_9/AUPRC_by_chrom/3_prime_UTR_variant/GPN_final.LogisticRegression.chrom.subset_from_all.csv
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,60,GPN_final.LogisticRegression.chrom.subset_from_all,0.32794198139025726
|
3 |
+
8,10,GPN_final.LogisticRegression.chrom.subset_from_all,0.14285714285714285
|
4 |
+
11,70,GPN_final.LogisticRegression.chrom.subset_from_all,0.15321532671804905
|
5 |
+
13,40,GPN_final.LogisticRegression.chrom.subset_from_all,0.3144230769230769
|
6 |
+
14,10,GPN_final.LogisticRegression.chrom.subset_from_all,0.1111111111111111
|
7 |
+
16,60,GPN_final.LogisticRegression.chrom.subset_from_all,0.27557132410073587
|
8 |
+
X,40,GPN_final.LogisticRegression.chrom.subset_from_all,0.1444139194139194
|
mendelian_traits_matched_9/AUPRC_by_chrom/3_prime_UTR_variant/GPN_final_LLR.minus.score.csv
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,60,GPN_final_LLR.minus.score,0.2775397840295538
|
3 |
+
8,10,GPN_final_LLR.minus.score,0.125
|
4 |
+
11,70,GPN_final_LLR.minus.score,0.13885669733822947
|
5 |
+
13,40,GPN_final_LLR.minus.score,0.13686371100164202
|
6 |
+
14,10,GPN_final_LLR.minus.score,0.1
|
7 |
+
16,60,GPN_final_LLR.minus.score,0.4027298542101173
|
8 |
+
X,40,GPN_final_LLR.minus.score,0.12670250896057347
|
mendelian_traits_matched_9/AUPRC_by_chrom/5_prime_UTR_variant/GPN_final.LogisticRegression.chrom.subset_from_all.csv
ADDED
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,80,GPN_final.LogisticRegression.chrom.subset_from_all,0.30865108362910665
|
3 |
+
2,30,GPN_final.LogisticRegression.chrom.subset_from_all,0.4288888888888889
|
4 |
+
3,30,GPN_final.LogisticRegression.chrom.subset_from_all,0.25
|
5 |
+
5,20,GPN_final.LogisticRegression.chrom.subset_from_all,0.12142857142857143
|
6 |
+
6,20,GPN_final.LogisticRegression.chrom.subset_from_all,0.5
|
7 |
+
7,20,GPN_final.LogisticRegression.chrom.subset_from_all,0.8333333333333333
|
8 |
+
9,40,GPN_final.LogisticRegression.chrom.subset_from_all,0.2908119658119658
|
9 |
+
10,110,GPN_final.LogisticRegression.chrom.subset_from_all,0.0856514621199276
|
10 |
+
11,230,GPN_final.LogisticRegression.chrom.subset_from_all,0.5880261831144116
|
11 |
+
12,10,GPN_final.LogisticRegression.chrom.subset_from_all,1.0
|
12 |
+
13,70,GPN_final.LogisticRegression.chrom.subset_from_all,0.16921438651516207
|
13 |
+
14,20,GPN_final.LogisticRegression.chrom.subset_from_all,1.0
|
14 |
+
17,20,GPN_final.LogisticRegression.chrom.subset_from_all,0.26785714285714285
|
15 |
+
19,260,GPN_final.LogisticRegression.chrom.subset_from_all,0.6429561156097049
|
16 |
+
20,10,GPN_final.LogisticRegression.chrom.subset_from_all,0.25
|
17 |
+
22,10,GPN_final.LogisticRegression.chrom.subset_from_all,0.3333333333333333
|
18 |
+
X,160,GPN_final.LogisticRegression.chrom.subset_from_all,0.2111844289825459
|
mendelian_traits_matched_9/AUPRC_by_chrom/5_prime_UTR_variant/GPN_final_LLR.minus.score.csv
ADDED
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,80,GPN_final_LLR.minus.score,0.3969771279936815
|
3 |
+
2,30,GPN_final_LLR.minus.score,0.4772727272727273
|
4 |
+
3,30,GPN_final_LLR.minus.score,0.2383838383838384
|
5 |
+
5,20,GPN_final_LLR.minus.score,0.10555555555555556
|
6 |
+
6,20,GPN_final_LLR.minus.score,0.5
|
7 |
+
7,20,GPN_final_LLR.minus.score,1.0
|
8 |
+
9,40,GPN_final_LLR.minus.score,0.279004329004329
|
9 |
+
10,110,GPN_final_LLR.minus.score,0.1266182568118525
|
10 |
+
11,230,GPN_final_LLR.minus.score,0.633999244890096
|
11 |
+
12,10,GPN_final_LLR.minus.score,1.0
|
12 |
+
13,70,GPN_final_LLR.minus.score,0.32905619454628754
|
13 |
+
14,20,GPN_final_LLR.minus.score,0.5769230769230769
|
14 |
+
17,20,GPN_final_LLR.minus.score,1.0
|
15 |
+
19,260,GPN_final_LLR.minus.score,0.964492260098489
|
16 |
+
20,10,GPN_final_LLR.minus.score,0.5
|
17 |
+
22,10,GPN_final_LLR.minus.score,1.0
|
18 |
+
X,160,GPN_final_LLR.minus.score,0.502673230308001
|
mendelian_traits_matched_9/AUPRC_by_chrom/all/Borzoi_L2_L2.plus.all.csv
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,210,Borzoi_L2_L2.plus.all,0.28886296587880606
|
3 |
+
2,230,Borzoi_L2_L2.plus.all,0.3596067340285779
|
4 |
+
3,310,Borzoi_L2_L2.plus.all,0.3141818502385483
|
5 |
+
5,20,Borzoi_L2_L2.plus.all,0.5666666666666667
|
6 |
+
6,30,Borzoi_L2_L2.plus.all,0.9166666666666665
|
7 |
+
7,210,Borzoi_L2_L2.plus.all,0.15898801542962734
|
8 |
+
8,70,Borzoi_L2_L2.plus.all,0.18653789713111749
|
9 |
+
9,240,Borzoi_L2_L2.plus.all,0.33766394392002164
|
10 |
+
10,190,Borzoi_L2_L2.plus.all,0.2734856856956791
|
11 |
+
11,480,Borzoi_L2_L2.plus.all,0.4786882293956932
|
12 |
+
12,30,Borzoi_L2_L2.plus.all,0.6031746031746031
|
13 |
+
13,210,Borzoi_L2_L2.plus.all,0.6413315208647058
|
14 |
+
14,40,Borzoi_L2_L2.plus.all,0.11122362357506668
|
15 |
+
16,80,Borzoi_L2_L2.plus.all,0.6892002296414061
|
16 |
+
17,60,Borzoi_L2_L2.plus.all,0.44615624027388734
|
17 |
+
19,400,Borzoi_L2_L2.plus.all,0.3618180913198451
|
18 |
+
20,50,Borzoi_L2_L2.plus.all,0.5916666666666667
|
19 |
+
22,20,Borzoi_L2_L2.plus.all,1.0
|
20 |
+
X,500,Borzoi_L2_L2.plus.all,0.6999088348317529
|
mendelian_traits_matched_9/AUPRC_by_chrom/all/CADD+GPN-MSA+Borzoi.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,210,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.4855946002421491
|
3 |
+
2,230,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.8955067691024454
|
4 |
+
3,310,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.85862068880898
|
5 |
+
5,20,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.5
|
6 |
+
6,30,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.7777777777777777
|
7 |
+
7,210,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.8481206549921971
|
8 |
+
8,70,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.6097960923499834
|
9 |
+
9,240,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.8975611588976531
|
10 |
+
10,190,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.4841439547412863
|
11 |
+
11,480,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.7075764354113238
|
12 |
+
12,30,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,1.0
|
13 |
+
13,210,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.7211992137936225
|
14 |
+
14,40,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.29
|
15 |
+
16,80,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,1.0
|
16 |
+
17,60,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.5147907647907648
|
17 |
+
19,400,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.6952124444401682
|
18 |
+
20,50,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.911111111111111
|
19 |
+
22,20,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,1.0
|
20 |
+
X,500,CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,0.7544007068580927
|
mendelian_traits_matched_9/AUPRC_by_chrom/all/CADD.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,210,CADD.LogisticRegression.chrom,0.7257063596694239
|
3 |
+
2,230,CADD.LogisticRegression.chrom,0.9665197806502153
|
4 |
+
3,310,CADD.LogisticRegression.chrom,0.9765572908840108
|
5 |
+
5,20,CADD.LogisticRegression.chrom,0.5833333333333333
|
6 |
+
6,30,CADD.LogisticRegression.chrom,0.5555555555555556
|
7 |
+
7,210,CADD.LogisticRegression.chrom,0.9736575481256333
|
8 |
+
8,70,CADD.LogisticRegression.chrom,0.9999999999999998
|
9 |
+
9,240,CADD.LogisticRegression.chrom,0.8931227978240486
|
10 |
+
10,190,CADD.LogisticRegression.chrom,0.6794411550535855
|
11 |
+
11,480,CADD.LogisticRegression.chrom,0.7848866173238882
|
12 |
+
12,30,CADD.LogisticRegression.chrom,1.0
|
13 |
+
13,210,CADD.LogisticRegression.chrom,0.8328422160569686
|
14 |
+
14,40,CADD.LogisticRegression.chrom,0.40929487179487173
|
15 |
+
16,80,CADD.LogisticRegression.chrom,0.6176300125313283
|
16 |
+
17,60,CADD.LogisticRegression.chrom,0.8787878787878787
|
17 |
+
19,400,CADD.LogisticRegression.chrom,0.9651715371679048
|
18 |
+
20,50,CADD.LogisticRegression.chrom,0.9666666666666666
|
19 |
+
22,20,CADD.LogisticRegression.chrom,1.0
|
20 |
+
X,500,CADD.LogisticRegression.chrom,0.9562419987319039
|
mendelian_traits_matched_9/AUPRC_by_chrom/all/Caduceus_InnerProduct.minus.score.csv
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,210,Caduceus_InnerProduct.minus.score,0.15135674297515564
|
3 |
+
2,230,Caduceus_InnerProduct.minus.score,0.1868821054855781
|
4 |
+
3,310,Caduceus_InnerProduct.minus.score,0.12523641541148628
|
5 |
+
5,20,Caduceus_InnerProduct.minus.score,0.5714285714285714
|
6 |
+
6,30,Caduceus_InnerProduct.minus.score,0.148109243697479
|
7 |
+
7,210,Caduceus_InnerProduct.minus.score,0.1362992099214087
|
8 |
+
8,70,Caduceus_InnerProduct.minus.score,0.07273278680908898
|
9 |
+
9,240,Caduceus_InnerProduct.minus.score,0.17812601057698604
|
10 |
+
10,190,Caduceus_InnerProduct.minus.score,0.08541813058340143
|
11 |
+
11,480,Caduceus_InnerProduct.minus.score,0.06924122946357933
|
12 |
+
12,30,Caduceus_InnerProduct.minus.score,0.7192982456140351
|
13 |
+
13,210,Caduceus_InnerProduct.minus.score,0.12148482624527741
|
14 |
+
14,40,Caduceus_InnerProduct.minus.score,0.10082877648667121
|
15 |
+
16,80,Caduceus_InnerProduct.minus.score,0.40122100122100124
|
16 |
+
17,60,Caduceus_InnerProduct.minus.score,0.12892854991366287
|
17 |
+
19,400,Caduceus_InnerProduct.minus.score,0.1264037656719949
|
18 |
+
20,50,Caduceus_InnerProduct.minus.score,0.12513960113960115
|
19 |
+
22,20,Caduceus_InnerProduct.minus.score,0.08496732026143791
|
20 |
+
X,500,Caduceus_InnerProduct.minus.score,0.07633369589997713
|
mendelian_traits_matched_9/AUPRC_by_chrom/all/Enformer_L2_L2.plus.all.csv
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,210,Enformer_L2_L2.plus.all,0.30910416369871774
|
3 |
+
2,230,Enformer_L2_L2.plus.all,0.4665020402459719
|
4 |
+
3,310,Enformer_L2_L2.plus.all,0.3772700312335094
|
5 |
+
5,20,Enformer_L2_L2.plus.all,0.6111111111111112
|
6 |
+
6,30,Enformer_L2_L2.plus.all,0.38690476190476186
|
7 |
+
7,210,Enformer_L2_L2.plus.all,0.17136377598150415
|
8 |
+
8,70,Enformer_L2_L2.plus.all,0.33039923039923036
|
9 |
+
9,240,Enformer_L2_L2.plus.all,0.4707809746637138
|
10 |
+
10,190,Enformer_L2_L2.plus.all,0.29224749578941595
|
11 |
+
11,480,Enformer_L2_L2.plus.all,0.38908372344555386
|
12 |
+
12,30,Enformer_L2_L2.plus.all,0.6055555555555555
|
13 |
+
13,210,Enformer_L2_L2.plus.all,0.5714071309956235
|
14 |
+
14,40,Enformer_L2_L2.plus.all,0.09039638792928267
|
15 |
+
16,80,Enformer_L2_L2.plus.all,0.5297491039426523
|
16 |
+
17,60,Enformer_L2_L2.plus.all,0.31738302958130543
|
17 |
+
19,400,Enformer_L2_L2.plus.all,0.31874993963198545
|
18 |
+
20,50,Enformer_L2_L2.plus.all,0.81
|
19 |
+
22,20,Enformer_L2_L2.plus.all,1.0
|
20 |
+
X,500,Enformer_L2_L2.plus.all,0.5899634261163853
|
mendelian_traits_matched_9/AUPRC_by_chrom/all/GPN-MSA+Borzoi.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,210,GPN-MSA+Borzoi.LogisticRegression.chrom,0.4665653878589111
|
3 |
+
2,230,GPN-MSA+Borzoi.LogisticRegression.chrom,0.6974294876980431
|
4 |
+
3,310,GPN-MSA+Borzoi.LogisticRegression.chrom,0.6893854062107326
|
5 |
+
5,20,GPN-MSA+Borzoi.LogisticRegression.chrom,0.5833333333333333
|
6 |
+
6,30,GPN-MSA+Borzoi.LogisticRegression.chrom,0.7777777777777777
|
7 |
+
7,210,GPN-MSA+Borzoi.LogisticRegression.chrom,0.9443677975483502
|
8 |
+
8,70,GPN-MSA+Borzoi.LogisticRegression.chrom,0.5006257631257631
|
9 |
+
9,240,GPN-MSA+Borzoi.LogisticRegression.chrom,0.8632795897330172
|
10 |
+
10,190,GPN-MSA+Borzoi.LogisticRegression.chrom,0.4095311295991844
|
11 |
+
11,480,GPN-MSA+Borzoi.LogisticRegression.chrom,0.6695885745416353
|
12 |
+
12,30,GPN-MSA+Borzoi.LogisticRegression.chrom,1.0
|
13 |
+
13,210,GPN-MSA+Borzoi.LogisticRegression.chrom,0.6455189351099319
|
14 |
+
14,40,GPN-MSA+Borzoi.LogisticRegression.chrom,0.32453703703703707
|
15 |
+
16,80,GPN-MSA+Borzoi.LogisticRegression.chrom,1.0
|
16 |
+
17,60,GPN-MSA+Borzoi.LogisticRegression.chrom,0.4469405539993775
|
17 |
+
19,400,GPN-MSA+Borzoi.LogisticRegression.chrom,0.8468649544703379
|
18 |
+
20,50,GPN-MSA+Borzoi.LogisticRegression.chrom,0.9666666666666666
|
19 |
+
22,20,GPN-MSA+Borzoi.LogisticRegression.chrom,1.0
|
20 |
+
X,500,GPN-MSA+Borzoi.LogisticRegression.chrom,0.8018668012712885
|
mendelian_traits_matched_9/AUPRC_by_chrom/all/GPN_final_Embeddings.plus.cosine_distance.csv
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,210,GPN_final_Embeddings.plus.cosine_distance,0.21575611839392667
|
3 |
+
2,230,GPN_final_Embeddings.plus.cosine_distance,0.6008864942095543
|
4 |
+
3,310,GPN_final_Embeddings.plus.cosine_distance,0.1462201255185057
|
5 |
+
5,20,GPN_final_Embeddings.plus.cosine_distance,0.26666666666666666
|
6 |
+
6,30,GPN_final_Embeddings.plus.cosine_distance,0.1267543859649123
|
7 |
+
7,210,GPN_final_Embeddings.plus.cosine_distance,0.24397501979302053
|
8 |
+
8,70,GPN_final_Embeddings.plus.cosine_distance,0.10920726355058152
|
9 |
+
9,240,GPN_final_Embeddings.plus.cosine_distance,0.13181656915996764
|
10 |
+
10,190,GPN_final_Embeddings.plus.cosine_distance,0.17926134964202034
|
11 |
+
11,480,GPN_final_Embeddings.plus.cosine_distance,0.1689561588765799
|
12 |
+
12,30,GPN_final_Embeddings.plus.cosine_distance,0.25111111111111106
|
13 |
+
13,210,GPN_final_Embeddings.plus.cosine_distance,0.3448736741606843
|
14 |
+
14,40,GPN_final_Embeddings.plus.cosine_distance,0.11314310689310689
|
15 |
+
16,80,GPN_final_Embeddings.plus.cosine_distance,0.3133924424005946
|
16 |
+
17,60,GPN_final_Embeddings.plus.cosine_distance,0.31800144300144295
|
17 |
+
19,400,GPN_final_Embeddings.plus.cosine_distance,0.21764854907341075
|
18 |
+
20,50,GPN_final_Embeddings.plus.cosine_distance,0.25990829346092503
|
19 |
+
22,20,GPN_final_Embeddings.plus.cosine_distance,0.5833333333333333
|
20 |
+
X,500,GPN_final_Embeddings.plus.cosine_distance,0.4125079658166293
|
mendelian_traits_matched_9/AUPRC_by_chrom/all/GPN_final_EuclideanDistance.plus.score.csv
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,210,GPN_final_EuclideanDistance.plus.score,0.2472531374032194
|
3 |
+
2,230,GPN_final_EuclideanDistance.plus.score,0.3513113012236947
|
4 |
+
3,310,GPN_final_EuclideanDistance.plus.score,0.1521004276323123
|
5 |
+
5,20,GPN_final_EuclideanDistance.plus.score,1.0
|
6 |
+
6,30,GPN_final_EuclideanDistance.plus.score,0.31313131313131315
|
7 |
+
7,210,GPN_final_EuclideanDistance.plus.score,0.216792071616217
|
8 |
+
8,70,GPN_final_EuclideanDistance.plus.score,0.14424746028808658
|
9 |
+
9,240,GPN_final_EuclideanDistance.plus.score,0.11554998898022437
|
10 |
+
10,190,GPN_final_EuclideanDistance.plus.score,0.15000800904075123
|
11 |
+
11,480,GPN_final_EuclideanDistance.plus.score,0.31419135278227406
|
12 |
+
12,30,GPN_final_EuclideanDistance.plus.score,0.5625
|
13 |
+
13,210,GPN_final_EuclideanDistance.plus.score,0.4865895015584955
|
14 |
+
14,40,GPN_final_EuclideanDistance.plus.score,0.14014408793820557
|
15 |
+
16,80,GPN_final_EuclideanDistance.plus.score,0.44501358695652177
|
16 |
+
17,60,GPN_final_EuclideanDistance.plus.score,0.6383351790328534
|
17 |
+
19,400,GPN_final_EuclideanDistance.plus.score,0.3277624422411202
|
18 |
+
20,50,GPN_final_EuclideanDistance.plus.score,0.5903703703703704
|
19 |
+
22,20,GPN_final_EuclideanDistance.plus.score,0.5833333333333333
|
20 |
+
X,500,GPN_final_EuclideanDistance.plus.score,0.6598823658035229
|
mendelian_traits_matched_9/AUPRC_by_chrom/all/NucleotideTransformer_Embeddings.plus.cosine_distance.csv
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,210,NucleotideTransformer_Embeddings.plus.cosine_distance,0.11957111405091744
|
3 |
+
2,230,NucleotideTransformer_Embeddings.plus.cosine_distance,0.3404828478859399
|
4 |
+
3,310,NucleotideTransformer_Embeddings.plus.cosine_distance,0.07269516850757896
|
5 |
+
5,20,NucleotideTransformer_Embeddings.plus.cosine_distance,0.41666666666666663
|
6 |
+
6,30,NucleotideTransformer_Embeddings.plus.cosine_distance,0.4769230769230769
|
7 |
+
7,210,NucleotideTransformer_Embeddings.plus.cosine_distance,0.1727256019842142
|
8 |
+
8,70,NucleotideTransformer_Embeddings.plus.cosine_distance,0.09760584488370529
|
9 |
+
9,240,NucleotideTransformer_Embeddings.plus.cosine_distance,0.10156282440755714
|
10 |
+
10,190,NucleotideTransformer_Embeddings.plus.cosine_distance,0.13707312377202724
|
11 |
+
11,480,NucleotideTransformer_Embeddings.plus.cosine_distance,0.1665377052633933
|
12 |
+
12,30,NucleotideTransformer_Embeddings.plus.cosine_distance,0.19771241830065361
|
13 |
+
13,210,NucleotideTransformer_Embeddings.plus.cosine_distance,0.10330033966650339
|
14 |
+
14,40,NucleotideTransformer_Embeddings.plus.cosine_distance,0.1579861111111111
|
15 |
+
16,80,NucleotideTransformer_Embeddings.plus.cosine_distance,0.3548751187537953
|
16 |
+
17,60,NucleotideTransformer_Embeddings.plus.cosine_distance,0.14038946355320084
|
17 |
+
19,400,NucleotideTransformer_Embeddings.plus.cosine_distance,0.23703158827958531
|
18 |
+
20,50,NucleotideTransformer_Embeddings.plus.cosine_distance,0.14201923076923076
|
19 |
+
22,20,NucleotideTransformer_Embeddings.plus.cosine_distance,0.26785714285714285
|
20 |
+
X,500,NucleotideTransformer_Embeddings.plus.cosine_distance,0.2544650875483345
|
mendelian_traits_matched_9/AUPRC_by_chrom/non_coding_transcript_exon_variant/GPN_final_LLR.minus.score.csv
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
2,150,GPN_final_LLR.minus.score,0.12009523359276793
|
3 |
+
3,250,GPN_final_LLR.minus.score,0.1462924533734105
|
4 |
+
9,200,GPN_final_LLR.minus.score,0.08608880730701247
|
5 |
+
11,40,GPN_final_LLR.minus.score,0.5317982456140351
|
6 |
+
13,30,GPN_final_LLR.minus.score,1.0
|
7 |
+
19,10,GPN_final_LLR.minus.score,0.25
|
8 |
+
22,10,GPN_final_LLR.minus.score,0.25
|
9 |
+
X,20,GPN_final_LLR.minus.score,0.6428571428571428
|
mendelian_traits_matched_9/AUPRC_by_chrom/nonexonic_AND_proximal/GPN_final_LLR.minus.score.csv
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
chrom,n,Model,AUPRC
|
2 |
+
1,70,GPN_final_LLR.minus.score,0.32663947248501335
|
3 |
+
10,30,GPN_final_LLR.minus.score,0.14215686274509803
|
4 |
+
11,110,GPN_final_LLR.minus.score,0.4512943392233827
|
5 |
+
12,10,GPN_final_LLR.minus.score,1.0
|
6 |
+
13,70,GPN_final_LLR.minus.score,0.5601790101790102
|
7 |
+
16,10,GPN_final_LLR.minus.score,0.125
|
8 |
+
17,30,GPN_final_LLR.minus.score,0.17735042735042736
|
9 |
+
19,130,GPN_final_LLR.minus.score,0.836111111111111
|
10 |
+
2,50,GPN_final_LLR.minus.score,1.0
|
11 |
+
20,40,GPN_final_LLR.minus.score,0.7934782608695652
|
12 |
+
3,10,GPN_final_LLR.minus.score,1.0
|
13 |
+
6,10,GPN_final_LLR.minus.score,0.16666666666666666
|
14 |
+
7,20,GPN_final_LLR.minus.score,0.41666666666666663
|
15 |
+
8,50,GPN_final_LLR.minus.score,0.27698901098901096
|
16 |
+
X,260,GPN_final_LLR.minus.score,0.7217791333956219
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/3_prime_UTR_variant/GPN_final.LogisticRegression.chrom.subset_from_all.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN_final.LogisticRegression.chrom.subset_from_all,AUPRC,0.23389321928581175,0.034673117024992504
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/3_prime_UTR_variant/GPN_final_LLR.minus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN_final_LLR.minus.score,AUPRC,0.21837515829843088,0.04843572997630987
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Borzoi.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
Borzoi.LogisticRegression.chrom,AUPRC,0.49298026682825974,0.03387962392290682
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Borzoi_L2_L2.plus.all.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
Borzoi_L2_L2.plus.all,AUPRC,0.4355656136169956,0.051597004283321764
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/CADD+Borzoi.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
CADD+Borzoi.LogisticRegression.chrom,AUPRC,0.7567548643334822,0.02800090306412582
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/CADD+GPN-MSA+Borzoi.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
CADD+GPN-MSA+Borzoi.LogisticRegression.chrom,AUPRC,0.739661869792865,0.03341660529784681
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/CADD.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
CADD.LogisticRegression.chrom,AUPRC,0.8746598261796723,0.03053334830628915
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/CADD.plus.RawScore.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
CADD.plus.RawScore,AUPRC,0.7121847808331817,0.038447877158699226
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Caduceus_Embeddings.minus.inner_product.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
Caduceus_Embeddings.minus.inner_product,AUPRC,0.131076433348165,0.016254752195469967
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Caduceus_Embeddings.plus.cosine_distance.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
Caduceus_Embeddings.plus.cosine_distance,AUPRC,0.13455900406076435,0.010093335540829028
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/Enformer.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
Enformer.LogisticRegression.chrom,AUPRC,0.4457897978393392,0.037976767408547156
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA+Borzoi.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN-MSA+Borzoi.LogisticRegression.chrom,AUPRC,0.7219609968170422,0.040220160541453186
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_Embeddings.minus.inner_product.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN-MSA_Embeddings.minus.inner_product,AUPRC,0.30112523795743745,0.03168988753882798
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_Embeddings.plus.cosine_distance.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN-MSA_Embeddings.plus.cosine_distance,AUPRC,0.20800225756771223,0.02098808509797134
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_Embeddings.plus.euclidean_distance.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN-MSA_Embeddings.plus.euclidean_distance,AUPRC,0.2069238590320193,0.020884292254464948
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_InnerProduct.minus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN-MSA_InnerProduct.minus.score,AUPRC,0.3011219010690259,0.031689359724673793
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_LLR.minus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN-MSA_LLR.minus.score,AUPRC,0.6944749707632801,0.0419068884991881
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_absLLR.plus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN-MSA_absLLR.plus.score,AUPRC,0.654222678718318,0.044972926205583436
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN_final.LogisticRegression.chrom,AUPRC,0.35284494920222204,0.0583212250339244
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final_Embeddings.plus.cosine_distance.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN_final_Embeddings.plus.cosine_distance,AUPRC,0.26339060037943085,0.03821887033915233
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final_InnerProduct.minus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN_final_InnerProduct.minus.score,AUPRC,0.16922307246758078,0.047496210894077816
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final_LLR.minus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN_final_LLR.minus.score,AUPRC,0.4216763931462392,0.0696638713065584
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN_final_absLLR.plus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
GPN_final_absLLR.plus.score,AUPRC,0.3788158878953516,0.06863303003742684
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA.LogisticRegression.chrom.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
HyenaDNA.LogisticRegression.chrom,AUPRC,0.14564822190132384,0.013889472455416422
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_Embeddings.minus.inner_product.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
HyenaDNA_Embeddings.minus.inner_product,AUPRC,0.16481698661406813,0.031015495478498096
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_Embeddings.plus.cosine_distance.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
HyenaDNA_Embeddings.plus.cosine_distance,AUPRC,0.116403099062234,0.013540904520628455
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_Embeddings.plus.euclidean_distance.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
HyenaDNA_Embeddings.plus.euclidean_distance,AUPRC,0.11689260373905239,0.014477215207015169
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_InnerProduct.minus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
HyenaDNA_InnerProduct.minus.score,AUPRC,0.164816865284936,0.03101554926698257
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/HyenaDNA_LLR.minus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
HyenaDNA_LLR.minus.score,AUPRC,0.1152052872349429,0.006084103442958281
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/NucleotideTransformer_Embeddings.minus.inner_product.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
NucleotideTransformer_Embeddings.minus.inner_product,AUPRC,0.18491586645094094,0.03892242955424739
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/NucleotideTransformer_Embeddings.plus.cosine_distance.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
NucleotideTransformer_Embeddings.plus.cosine_distance,AUPRC,0.18559760988782165,0.02214462833081281
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/NucleotideTransformer_InnerProduct.minus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
NucleotideTransformer_InnerProduct.minus.score,AUPRC,0.18486365621773523,0.03888299929015381
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/all/NucleotideTransformer_absLLR.plus.score.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
NucleotideTransformer_absLLR.plus.score,AUPRC,0.0980640932373892,0.006411086678697549
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/no_cadd_overlap/Borzoi_L2_L2.plus.all.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
Borzoi_L2_L2.plus.all,AUPRC,0.42077578278371996,0.056588693438561044
|
mendelian_traits_matched_9/AUPRC_by_chrom_weighted_average/no_cadd_overlap/CADD.LogisticRegression.chrom.subset_from_all.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
model,metric,score,se
|
2 |
+
CADD.LogisticRegression.chrom.subset_from_all,AUPRC,0.8726172101949277,0.03181353647521414
|