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@@ -303,7 +303,7 @@ Function predictions are generated using `DeepFRI` across
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  Generating train split: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1332900735/1332900735 [13:11<00:00, 1684288.89 examples/s]
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  Loading dataset shards: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 219/219 [01:22<00:00, 2.66it/s]
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- this loads the `>1.3B` function predictions for all 211069 targets for the GO and EC ontology terms.
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  The predictions are stored in long format, but can be easily converted to a wide format using pandas:
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  >>> import pandas
@@ -312,7 +312,8 @@ The predictions are stored in long format, but can be easily converted to a wide
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  columns = "term_id",
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  index = "id",
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  values = "Y_hat")
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- >>> dataset_function_prediction_wide[1:3, 1:3]
 
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  ## Dataset Details
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  Generating train split: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1332900735/1332900735 [13:11<00:00, 1684288.89 examples/s]
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  Loading dataset shards: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 219/219 [01:22<00:00, 2.66it/s]
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+ this loads the `>1.3B` function predictions for all `211069` targets across `6315` GO and EC ontology terms.
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  The predictions are stored in long format, but can be easily converted to a wide format using pandas:
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  >>> import pandas
 
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  columns = "term_id",
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  index = "id",
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  values = "Y_hat")
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+ >>> dataset_function_prediction_wide.shape
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+ (211069, 6315)
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  ## Dataset Details
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