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HSA0000001.1
ATF1
HSA0000002.1
ATF3
HSA0000003.1
JUN
HSA0000004.1
MYC
HSA0000005.1
CTCF
HSA0000006.1
CTCFL
HSA0000007.1
EGR1
HSA0000008.1
ELK1
HSA0000010.1
ETS1
HSA0000011.1
FOS
HSA0000012.1
GABPA
HSA0000013.1
GATA1
HSA0000014.1
GATA2
HSA0000015.1
KLF4
HSA0000016.1
MAFF
HSA0000017.1
MAFK
HSA0000018.1
MEF2C
HSA0000019.1
RELA
HSA0000020.1
NFYA
HSA0000021.1
NFYB
HSA0000023.1
NRF1
HSA0000024.1
REST
HSA0000025.1
TP53
HSA0000026.1
POU5F1
HSA0000027.1
SPI1
HSA0000028.1
REST
HSA0000029.1
RFX5
HSA0000030.1
RUNX3
HSA0000031.1
SP1
HSA0000032.1
SP2
HSA0000033.1
SP4
HSA0000034.1
SREBF1
HSA0000035.1
STAT1
HSA0000036.1
STAT3
HSA0000037.1
TCF12
HSA0000039.1
TEAD4
HSA0000040.1
USF1
HSA0000041.1
USF2
HSA0000042.1
YY1
HSA0000043.1
ZEB1
HSA0000045.1
BACH1
HSA0000046.1
BDP1
HSA0000047.1
BRCA1
HSA0000048.1
BRF1
HSA0000049.1
CREB1
HSA0000050.1
ELF1
HSA0000051.1
ESRRA
HSA0000052.1
IRF3
HSA0000053.1
JUNB
HSA0000054.1
NANOG
HSA0000055.1
RAD21
HSA0000056.1
POLR3A
HSA0000057.1
SIX5
HSA0000058.1
TAF1
HSA0000059.1
TAF7
HSA0000060.1
ZNF274
HSA0000061.1
TBP
HSA0000062.1
ELK4
HSA0000063.1
GTF3C2
HSA0000064.1
SRF
HSA0000066.1
NR3C1
HSA0000068.1
E2F1
HSA0000069.1
PRDM1
HSA0000070.1
CEBPB
HSA0000071.1
HNF4A
HSA0000072.1
CREB1
HSA0000073.1
BHLHE40
HSA0000074.1
TFAP2A
HSA0000075.1
TFAP2C
HSA0000076.1
PUM1
HSA0000077.1
FOSL2
HSA0000078.1
IRF1
HSA0000079.1
XBP1
HSA0000080.1
XBP1
HSA0000081.1
ZEB2
HSA0000082.1
NR1H3
HSA0000083.1
PPARG
HSA0000084.1
RBPJ
HSA0000085.1
SMAD4
HSA0000086.1
RXRA
MMU0000009.1
Esrrb
MMU0000022.1
Mycn
MMU0000038.1
Tfcp2l1
MMU0000044.1
Zfx
MMU0000070.1
Irf8
Select a model on the left pane to visualise the model specifications.
Logo

The logo as it was extracted from the trainings-data.

Quality and thresholds

The quality of the models was validated by identifying TFBS on an external dataset.

The validation was performed on the pure dataset and on the dataset previously filtered by a PWM. �

Features

The model uses bio-physical-features to identify possible TFBS. The following table presents these features. The list can be arranged by:
- the position where the feature occurred
- the feature itself

Logo

The logo as it was extracted from the trainings-data.

-logo
Quality

The quality of the models was validated by identifying TFBS on an external dataset.

The validation was performed on the pure dataset and on the dataset previously filtered by a PWM. �

Less...
Quality Pure
Max. Recall
Max. Recall Threshold
Max. Precision
Max. Precision Threshold
Average Threshold
Max. F-Measure
Max. F-Measure Threshold
Area under the ROC-curve
-rc_pure
Quality Filter
Max. Recall
Max. Recall Threshold
Max. Precision
Max. Precision Threshold
Average Threshold
Max. F-Measure
Max. F-Measure Threshold
Area under the ROC-curve
-rc_filter
Quality Cross-Validation
Max. Recall
Max. Recall Threshold
Max. Precision
Max. Precision Threshold
Average Threshold
Max. F-Measure
Max. F-Measure Threshold
Area under the ROC-curve
Features

The model uses bio-physical-features to identify possible TFBS. The following table presents these features. The list can be arranged by:
- the position where the feature occurred
- the feature itself

Less...