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import yaml
import pandas as pd
import tqdm
import semantic_similarity_infer as ssi
import target_family_classifier as tfc
import function_predictor as fp
import binding_affinity_estimator as bae
print("\n\nPROBE (Protein RepresentatiOn Benchmark) run is started...\n\n")
with open('probe_config.yaml') as f:
args = yaml.load(f, Loader=yaml.FullLoader)
if args["benchmark"] not in ["similarity","family","function","affinity","all"]:
parser.error('At least one benchmark type should be selected')
print(args)
def load_representation(multi_col_representation_vector_file_path):
multi_col_representation_vector = pd.read_csv(multi_col_representation_vector_file_path)
vals = multi_col_representation_vector.iloc[:,1:(len(multi_col_representation_vector.columns))]
original_values_as_df = pd.DataFrame({'Entry': pd.Series([], dtype='str'),'Vector': pd.Series([], dtype='object')})
for index, row in tqdm.tqdm(vals.iterrows(), total = len(vals)):
list_of_floats = [float(item) for item in list(row)]
original_values_as_df.loc[index] = [multi_col_representation_vector.iloc[index]['Entry']] + [list_of_floats]
return original_values_as_df
if args["benchmark"] in ["similarity","function","all"]:
print("\nRepresentation vectors are loading...\n")
representation_dataframe = load_representation(args["representation_file_human"])
if args["benchmark"] in ["similarity","all"]:
print("\nSemantic similarity Inference Benchmark is running...\n")
ssi.representation_dataframe = representation_dataframe
ssi.representation_name = args["representation_name"]
ssi.protein_names = ssi.representation_dataframe['Entry'].tolist()
ssi.similarity_tasks = args["similarity_tasks"]
ssi.detailed_output = args["detailed_output"]
ssi.calculate_all_correlations()
if args["benchmark"] in ["function","all"]:
print("\n\nOntology-based protein function prediction benchmark is running...\n")
fp.aspect_type = args["function_prediction_aspect"]
fp.dataset_type = args["function_prediction_dataset"]
fp.representation_dataframe = representation_dataframe
fp.representation_name = args["representation_name"]
fp.detailed_output = args["detailed_output"]
fp.pred_output()
if args["benchmark"] in ["family","all"]:
print("\n\nDrug target protein family classification benchmark is running...\n")
tfc.representation_path = args["representation_file_human"]
tfc.representation_name = args["representation_name"]
tfc.detailed_output = args["detailed_output"]
for dataset in args["family_prediction_dataset"]:
tfc.score_protein_rep(dataset)
if args["benchmark"] in ["affinity","all"]:
print("\n\nProtein-protein binding affinity estimation benchmark is running...\n")
bae.skempi_vectors_path = args["representation_file_affinity"]
bae.representation_name = args["representation_name"]
bae.predict_affinities_and_report_results()
print("\n\nPROBE (Protein RepresentatiOn Benchmark) run is finished...\n")
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