| import pandas as pd
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| from utils import Ontology
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| def prop(df):
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| prop_annotations = []
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| for i, row in df.iterrows():
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| annot_set = set()
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| annots = row['GO_label']
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| for go_id in annots:
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| annot_set |= godb.get_anchestors(go_id)
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| annots = list(annot_set)
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| prop_annotations.append(annots)
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| df['prop_annotations'] = prop_annotations
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| return df
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| godb = Ontology(f'/cluster/home/wenkai/LAVIS/data/go1.4-basic.obo', with_rels=True)
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| case_mf = pd.read_csv('/cluster/home/wenkai/LAVIS/data/pretrain/cases_mf.csv', sep='|')
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| case_bp = pd.read_csv('/cluster/home/wenkai/LAVIS/data/pretrain/cases_bp.csv', sep='|')
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| case_bp['GO_label'] = case_bp['GO_label'].apply(lambda x: [i.strip() for i in x.split(';')])
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| case_bp = prop(case_bp)
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| case_bp['GO_label'] = case_bp['GO_label'].apply(lambda x: '; '.join(x))
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| case_bp['prop_annotations'] = case_bp['prop_annotations'].apply(lambda x: '; '.join(x))
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| case_bp[['name', 'protein', 'function', 'GO_label', 'id', 'prompt', 'prop_annotations']].to_pickle('/cluster/home/wenkai/deepgo2/data/bp/cases_data.pkl')
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| case_mf['GO_label'] = case_mf['GO_label'].apply(lambda x: [i.strip() for i in x.split(';')])
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| case_mf = prop(case_mf)
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| case_mf['GO_label'] = case_mf['GO_label'].apply(lambda x: '; '.join(x))
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| case_mf['prop_annotations'] = case_mf['prop_annotations'].apply(lambda x: '; '.join(x))
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| case_bp['GO_label'] = case_bp['GO_label'].apply(lambda x: [i.strip() for i in x.split(';')])
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| case_bp = prop(case_bp)
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| case_mf[['name', 'protein', 'function', 'GO_label', 'id', 'prompt', 'prop_annotations']].to_pickle('/cluster/home/wenkai/deepgo2/data/mf/cases_data_445772.pkl')
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