def get_transmir_gold_ann_set(goldpath, entitytype):
logging.info("loading gold standard... {}".format(goldpath))
gold_entities = set()
gold_relations = {}
with open(goldpath, 'r') as goldfile:
for l in goldfile:
tsv = l.strip().split("\t")
if tsv[-1].lower() == "human":
# print "gold standard", tsv[8], tsv[0], tsv[3], entitytype
pmids = tsv[8].split(";")
norm_mirna = mirna_graph.map_label(tsv[3])
if norm_mirna < 99:
norm_mirna[0] = tsv[3]
norm_gene = get_uniprot_name(tsv[0])
for did in pmids:
if entitytype == "mirna":
gold_entities.add(
("PMID" + did, "0", "0", norm_mirna[0].lower()))
elif entitytype == "protein":
gold_entities.add(
("PMID" + did, "0", "0", norm_gene[0].lower()))
gold_relations[("PMID" + did, norm_mirna[0], norm_gene[0],
norm_mirna[0] + "=>" +
norm_gene[0])] = [tsv[3] + "=>" + tsv[0]]
#gold_relations[("PMID", norm_mirna[0], norm_gene[0], norm_mirna[0] + "=>" + norm_gene[0])] = [tsv[3] + "=>" + tsv[0]]
# print gold_entities
return gold_entities, gold_relations
def get_ddi_mirna_gold_ann_set(goldpath, entitytype, pairtype):
logging.info("loading gold standard... {}".format(goldpath))
gold_offsets = set()
gold_pairs = set()
original_id_to_offset = {}
original_id_to_text = {}
tree = ET.parse(goldpath)
#with codecs.open(goldpath, 'r', 'utf-8') as xml:
root = tree.getroot()
#parse DDI corpus file
t = time.time()
# root = ET.fromstring(xml.read())
rfile = open("corpora/miRNACorpus/miRNAcorpus_relations.txt", 'w')
for doc in root.findall("document"):
did = doc.get('id')
doctext = ""
for sentence in doc.findall('sentence'):
sentence_text = sentence.get('text')
#sentence_text = sentence_text.replace('\r\n', ' ')
for entity in sentence.findall('entity'):
entity_offset = entity.get('charOffset')
if ";" in entity_offset:
continue
offsets = entity_offset.split("-")
start, end = int(offsets[0]) + len(doctext), int(
offsets[1]) + len(doctext) + 1
etype = type_match.get(entity.get("type"))
original_id_to_offset[entity.get("id")] = (start, end)
original_id_to_text[entity.get("id")] = entity.get("text")
#print this_sentence.text[offsets[0]:offsets[-1]], entity.get("text")
#if "protein" in entity_type.lower() or "mirna" in entity_type.lower():
if etype == entitytype:
gold_offsets.add((did, start, end, entity.get("text")))
for pair in sentence.findall('pair'):
try:
p_type = type_match[pair.get("type")]
except KeyError:
continue
p_true = pair.get("interaction")
if p_type == pairtype and p_true == "True":
gold_pair = (did, original_id_to_offset[pair.get("e1")],
original_id_to_offset[pair.get("e2")],
"{}={}>{}".format(
original_id_to_text[pair.get("e1")],
p_type,
original_id_to_text[pair.get("e2")]))
gold_pairs.add(gold_pair)
norm_mirna = mirna_graph.map_label(
original_id_to_text[pair.get("e1")])
if norm_mirna < 99:
norm_mirna[0] = original_id_to_text[pair.get("e1")]
norm_gene = get_uniprot_name(
original_id_to_text[pair.get("e2")])
rfile.write("{}\t{}\n".format(norm_mirna[0], norm_gene[0]))
doctext += " " + sentence_text # generate the full text of this document
# logging.debug(gold_pairs)
rfile.close()
return gold_offsets, gold_pairs
def get_ddi_mirna_gold_ann_set(goldpath, entitytype, pairtype):
logging.info("loading gold standard... {}".format(goldpath))
gold_offsets = set()
gold_pairs = set()
original_id_to_offset = {}
original_id_to_text = {}
tree = ET.parse(goldpath)
#with codecs.open(goldpath, 'r', 'utf-8') as xml:
root = tree.getroot()
#parse DDI corpus file
t = time.time()
# root = ET.fromstring(xml.read())
rfile = open("corpora/miRNACorpus/miRNAcorpus_relations.txt", 'w')
for doc in root.findall("document"):
did = doc.get('id')
doctext = ""
for sentence in doc.findall('sentence'):
sentence_text = sentence.get('text')
#sentence_text = sentence_text.replace('\r\n', ' ')
for entity in sentence.findall('entity'):
entity_offset = entity.get('charOffset')
if ";" in entity_offset:
continue
offsets = entity_offset.split("-")
start, end = int(offsets[0]) + len(doctext), int(offsets[1]) + len(doctext) + 1
etype = type_match.get(entity.get("type"))
original_id_to_offset[entity.get("id")] = (start, end)
original_id_to_text[entity.get("id")] = entity.get("text")
#print this_sentence.text[offsets[0]:offsets[-1]], entity.get("text")
#if "protein" in entity_type.lower() or "mirna" in entity_type.lower():
if etype == entitytype:
gold_offsets.add((did, start, end, entity.get("text")))
for pair in sentence.findall('pair'):
try:
p_type = type_match[pair.get("type")]
except KeyError:
continue
p_true = pair.get("interaction")
if p_type == pairtype and p_true == "True":
gold_pair = (did, original_id_to_offset[pair.get("e1")], original_id_to_offset[pair.get("e2")],
"{}={}>{}".format(original_id_to_text[pair.get("e1")], p_type, original_id_to_text[pair.get("e2")]))
gold_pairs.add(gold_pair)
norm_mirna = mirna_graph.map_label(original_id_to_text[pair.get("e1")])
if norm_mirna < 99:
norm_mirna[0] = original_id_to_text[pair.get("e1")]
norm_gene = get_uniprot_name(original_id_to_text[pair.get("e2")])
rfile.write("{}\t{}\n".format(norm_mirna[0], norm_gene[0]))
doctext += " " + sentence_text # generate the full text of this document
# logging.debug(gold_pairs)
rfile.close()
return gold_offsets, gold_pairs
def get_mirtex_gold_ann_set(goldpath, entitytype, pairtype):
logging.info("loading gold standard... {}".format(goldpath))
annfiles = [
goldpath + '/' + f for f in os.listdir(goldpath) if f.endswith('.ann')
]
gold_offsets = set()
for current, f in enumerate(annfiles):
did = f.split(".")[0]
with open(f, 'r') as txt:
for line in txt:
if line.startswith("T"):
tid, ann, etext = line.strip().split("\t")
etype, dstart, dend = ann.split(" ")
if entitytype == type_match[etype]:
dstart, dend = int(dstart), int(dend)
gold_offsets.add((did, dstart, dend, etext))
gold_relations = {}
with open(goldpath + "/" + "annotations.tsv") as afile:
for l in afile:
v = l.strip().split("\t")
if len(v) < 3:
continue
did = goldpath + '/' + v[0]
# logging.info("{} {} {}".format(did, pairtype, v[-1]))
if pairtype == "all" or type_match.get(" ".join(
v[-2:])) == pairtype:
e1 = v[1].split(";")
for mirna in e1:
mirna = mirna.replace('"', '')
# logging.info(mirna)
norm_mirna = mirna_graph.map_label(mirna)
if norm_mirna < 99:
norm_mirna[0] = mirna
e2 = v[2].split(";")
for gene in e2:
gene = gene.replace('"', '')
# logging.info(gene)
norm_gene = get_uniprot_name(gene)
#gold_relations.add((did, norm_mirna[0], norm_gene[0]))
gold_relations[(did, norm_mirna[0], norm_gene[0],
norm_mirna[0] + "=>" +
norm_gene[0])] = []
return gold_offsets, gold_relations
def get_mirtex_gold_ann_set(goldpath, entitytype, pairtype):
logging.info("loading gold standard... {}".format(goldpath))
annfiles = [goldpath + '/' + f for f in os.listdir(goldpath) if f.endswith('.ann')]
gold_offsets = set()
for current, f in enumerate(annfiles):
did = f.split(".")[0]
with open(f, 'r') as txt:
for line in txt:
if line.startswith("T"):
tid, ann, etext = line.strip().split("\t")
etype, dstart, dend = ann.split(" ")
if entitytype == type_match[etype]:
dstart, dend = int(dstart), int(dend)
gold_offsets.add((did, dstart, dend, etext))
gold_relations = {}
with open(goldpath + "/" + "annotations.tsv") as afile:
for l in afile:
v = l.strip().split("\t")
if len(v) < 3:
continue
did = goldpath + '/' + v[0]
# logging.info("{} {} {}".format(did, pairtype, v[-1]))
if pairtype == "all" or type_match.get(" ".join(v[-2:])) == pairtype:
e1 = v[1].split(";")
for mirna in e1:
mirna = mirna.replace('"', '')
# logging.info(mirna)
norm_mirna = mirna_graph.map_label(mirna)
if norm_mirna < 99:
norm_mirna[0] = mirna
e2 = v[2].split(";")
for gene in e2:
gene = gene.replace('"', '')
# logging.info(gene)
norm_gene = get_uniprot_name(gene)
#gold_relations.add((did, norm_mirna[0], norm_gene[0]))
gold_relations[(did, norm_mirna[0], norm_gene[0], norm_mirna[0] + "=>" + norm_gene[0])] = []
return gold_offsets, gold_relations
def get_transmir_gold_ann_set(goldpath, entitytype):
logging.info("loading gold standard... {}".format(goldpath))
gold_entities = set()
gold_relations = {}
with open(goldpath, 'r') as goldfile:
for l in goldfile:
tsv = l.strip().split("\t")
if tsv[-1].lower() == "human":
# print "gold standard", tsv[8], tsv[0], tsv[3], entitytype
pmids = tsv[8].split(";")
norm_mirna = mirna_graph.map_label(tsv[3])
if norm_mirna < 99:
norm_mirna[0] = tsv[3]
norm_gene = get_uniprot_name(tsv[0])
for did in pmids:
if entitytype == "mirna":
gold_entities.add(("PMID" + did, "0", "0", norm_mirna[0].lower()))
elif entitytype == "protein":
gold_entities.add(("PMID" + did, "0", "0", norm_gene[0].lower()))
gold_relations[("PMID" + did, norm_mirna[0], norm_gene[0], norm_mirna[0] + "=>" + norm_gene[0])] = [tsv[3] + "=>" + tsv[0]]
#gold_relations[("PMID", norm_mirna[0], norm_gene[0], norm_mirna[0] + "=>" + norm_gene[0])] = [tsv[3] + "=>" + tsv[0]]
# print gold_entities
return gold_entities, gold_relations
# read transmir database and generate corpus
from text.mirna_entity import mirna_graph
from text.protein_entity import get_uniprot_name
db_name = "data/transmir_v1.2.tsv"
tfs = set()
diseases = set()
funcs = set()
pmids = set()
mirnas = {} # mirname: (function, disease)
entries = {} # (tfname, mirname): active
with open(db_name, 'r') as dbfile:
for line in dbfile:
tsv = line.strip().split("\t")
tfname = get_uniprot_name(tsv[0])
mirname = mirna_graph.map_label(tsv[3])
tfname = tfname[0]
mirname = mirname[0]
func = tsv[5].split(";")
disease = tsv[6].split(";")
active = tsv[7]
pmid = tsv[8].split(";")
if tsv[-1].lower() == "human":
tfs.add(tfname.replace("-", "")) # uniform TF names
for f in func:
funcs.add(f.strip())
for d in disease:
if d != "see HMDD (http://cmbi.bjmu.edu.cn/hmdd)":
diseases.add(d.strip())
for p in pmid:
pmids.add(p.strip())
# read transmir database and generate corpus
from text.mirna_entity import mirna_graph
from text.protein_entity import get_uniprot_name
db_name = "data/transmir_v1.2.tsv"
tfs = set()
diseases = set()
funcs = set()
pmids = set()
mirnas = {} # mirname: (function, disease)
entries = {} # (tfname, mirname): active
with open(db_name, 'r') as dbfile:
for line in dbfile:
tsv = line.strip().split("\t")
tfname = get_uniprot_name(tsv[0])
mirname = mirna_graph.map_label(tsv[3])
tfname = tfname[0]
mirname = mirname[0]
func = tsv[5].split(";")
disease = tsv[6].split(";")
active = tsv[7]
pmid = tsv[8].split(";")
if tsv[-1].lower() == "human":
tfs.add(tfname.replace("-", "")) # uniform TF names
for f in func:
funcs.add(f.strip())
for d in disease:
if d != "see HMDD (http://cmbi.bjmu.edu.cn/hmdd)":
diseases.add(d.strip())
for p in pmid:
pmids.add(p.strip())