def eval_func(space):
# --- bp ---
bp = biolib.Ontology(filemanager.Load.ontology_similarity_data("BP"))
bp.filter_gene_names(genes_in_data)
bp.convert_nan_to_zero()
bp.apply_threshold(space["threshold"])
if space["binarize"]:
bp.binarize()
bp.to_np(genes_in_data)
bp = algorithms.Adjacency(bp.data, genes_in_data)
return evaluator.evaluate(bp)
# data = json.load(f)
########################################################################################################################
#
# ---------Select genes 2 use in common---------------
# --- using most restricting params
d = diseaseslib.DiGeNet(group_by="by_diseases")
gl = diseaseslib.GeneLinkage(gene_code="entrezid", gen_interval_len=priorization_params["linkage_interval"])
# --- load info for networks ---
# ppi, bp
ppi = biolib.PPI(filemanager.Load.ppi())
bp = biolib.Ontology(filemanager.Load.ontology_similarity_data("BP"))
gtex_genes = biolib.EXP().gene_names
# --- finding genes in common ---
genes_in_data = diseaseslib.get_gene_universe(gene_linkage=gl,
dict_of_networks={},
list_of_list_of_genes=[ppi.gene_names, bp.gene_names, gtex_genes],
mode="intersect")
print("Genes in common: {}".format(len(genes_in_data)))
# --- ppi ---
ppi.filter_gene_names(genes_in_data)
ppi.apply_threshold(ppi_params["threshold"])
if ppi_params["binarize"]:
ppi.binarize()