ct = CoopTrain(df,
corelen=4,
flip_th=True,
positive_cores=["GGAA", "GGAT"])
# using custom imads model
imads_paths = [
"input/site_models/imads_models/Ets1_w12_GGAA.model",
"input/site_models/imads_models/Ets1_w12_GGAT.model"
]
imads_cores = ["GGAA", "GGAT"]
imads_models = [
iMADSModel(path, core, 12, [1, 2, 3])
for path, core in zip(imads_paths, imads_cores)
]
imads = iMADS(imads_models, 0.19) # 0.2128
# get the features from the CoopTrain class
feature_dict = {
"distance": {
"type": "numerical"
},
"orientation": {
"positive_cores": ["GGAA", "GGAT"],
"one_hot": True
},
"affinity": {
"imads": imads
}
}
train = ct.get_feature_all(feature_dict)
import os
os.chdir("../..")
from chip2probe.sitespredict.imads import iMADS
from chip2probe.sitespredict.imadsmodel import iMADSModel
from chip2probe.sitespredict.pbmescore import PBMEscore
from chip2probe.sitespredict.dnasequence import DNASequence
if __name__ == "__main__":
imads12_paths = [
"input/site_models/imads_models/Ets1_w12_GGAA.model",
"input/site_models/imads_models/Ets1_w12_GGAT.model"
]
imads12_cores = ["GGAA", "GGAT"]
imads12_models = [
iMADSModel(path, core, 12, [1, 2, 3])
for path, core in zip(imads12_paths, imads12_cores)
]
imads12 = iMADS(imads12_models, 0.19) # 0.2128
escore = PBMEscore("input/site_models/escores/Ets1_8mers_11111111.txt")
seq = DNASequence("CAGCTGGCCGGAACCTGCGTCCCCTTCCCCCGCCGC", imads12, escore)
print(seq.sites)