def LF_husband_wife_left_window(c):
if len(spouses.intersection(get_left_tokens(c[0], window=2))) > 0:
return 1
elif len(spouses.intersection(get_left_tokens(c[1], window=2))) > 0:
return 1
else:
return 0
def LF_family_left_window(c):
if len(family.intersection(get_left_tokens(c[0], window=2))) > 0:
return -1
elif len(family.intersection(get_left_tokens(c[1], window=2))) > 0:
return -1
else:
return 0
def LF_husband_wife_left_window(c):
if len(spouses.intersection(get_left_tokens(c[0], window=2))) > 0:
return 1
elif len(spouses.intersection(get_left_tokens(c[1], window=2))) > 0:
return 1
else:
return 0
def LF_family_left_window(c):
if len(family.intersection(get_left_tokens(c[0], window=2))) > 0:
return -1
elif len(family.intersection(get_left_tokens(c[1], window=2))) > 0:
return -1
else:
return 0
def LF_model_organisms(c):
"""
If mentions model organism then c[1] should be a gene
"""
if len(model_organisms.intersection(get_left_tokens(c[1]))) > 0:
return 1
if len(model_organisms.intersection(get_left_tokens(c[1]))) > 0:
return 1
return 0
def LF_GiG_CELL_IDENTIFICATIONS(c):
gene1_tokens = list(get_left_tokens(c[0], window=5)) + list(get_right_tokens(c[0], window=5))
gene2_tokens = list(get_left_tokens(c[0], window=5)) + list(get_right_tokens(c[0], window=5))
if re.search(ltp(cell_indications), " ".join(gene1_tokens), flags=re.I):
return -1
elif re.search(ltp(cell_indications), " ".join(gene2_tokens), flags=re.I):
return -1
else:
return 0
def LF_GiG_GENE_IDENTIFIERS(c):
cand1_text = " ".join(list(get_left_tokens(c[0], window=5)) + list(get_right_tokens(c[0], window=5)))
cand2_text = " ".join(list(get_left_tokens(c[1], window=5)) + list(get_right_tokens(c[1], window=5)))
if re.search(ltp(gene_identifiers), cand1_text, flags=re.I):
return 1
elif re.search(ltp(gene_identifiers), cand2_text, flags=re.I):
return 1
else:
return 0
def LF_GiG_BINDING_IDENTIFICATIONS(c):
gene1_tokens = list(get_left_tokens(c[0], window=5)) + list(get_right_tokens(c[0], window=5))
gene2_tokens = list(get_left_tokens(c[0], window=5)) + list(get_right_tokens(c[0], window=5))
if re.search(ltp(binding_identifiers), " ".join(gene1_tokens), flags=re.I):
return 1
elif re.search(ltp(binding_identifiers), " ".join(gene2_tokens), flags=re.I):
return 1
elif re.search(ltp(binding_identifiers), get_text_between(c), flags=re.I):
return 1
else:
return 0
def LF_positive(c):
if (len(positive.intersection(get_between_tokens(c))) > 0) and not neg_nearby(c):
return 1
elif (len(positive.intersection(get_left_tokens(c[0], window=20))) > 0) and not neg_nearby(c):
return 1
elif (len(positive.intersection(get_left_tokens(c[1], window=20))) > 0) and not neg_nearby(c):
return 1
elif (len(positive.intersection(get_right_tokens(c[0], window=20))) > 0) and not neg_nearby(c):
return 1
elif (len(positive.intersection(get_right_tokens(c[1], window=20))) > 0) and not neg_nearby(c):
return 1
else:
return 0
def LF_neg_assertions(c):
if (len(negative.intersection(get_between_tokens(c))) > 0):
return -1
elif (len(negative.intersection(get_left_tokens(c[0], window=10))) > 0):
return -1
elif (len(negative.intersection(get_left_tokens(c[1], window=20))) > 0):
return -1
elif (len(negative.intersection(get_right_tokens(c[0], window=20))) > 0):
return -1
# elif (len(negative.intersection(get_right_tokens(c[1], window=20))) > 0):
# return -1
else:
return 0
def LF_other_verbs(c):
if (len(other_verbs.intersection(get_between_tokens(c))) > 0) and not neg_nearby(c):
return 1
elif (len(other_verbs.intersection(get_left_tokens(c[0], window=20))) > 0) and not neg_nearby(c):
return 1
elif (len(other_verbs.intersection(get_left_tokens(c[1], window=20))) > 0) and not neg_nearby(c):
return 1
elif (len(other_verbs.intersection(get_right_tokens(c[0], window=20))) > 0) and not neg_nearby(c):
return 1
elif (len(other_verbs.intersection(get_right_tokens(c[1], window=20))) > 0) and not neg_nearby(c):
return 1
else:
return 0
def LF_isolate(c):
if len(isolate.intersection(get_between_tokens(c))) > 0 and not neg_nearby(c):
return 1
elif len(isolate.intersection(get_left_tokens(c[0], window=20))) > 0 and not neg_nearby(c):
return 1
elif len(isolate.intersection(get_left_tokens(c[1], window=20))) > 0 and not neg_nearby(c):
return 1
elif len(isolate.intersection(get_right_tokens(c[0], window=20))) > 0 and not neg_nearby(c):
return 1
elif len(isolate.intersection(get_right_tokens(c[1], window=20))) > 0 and not neg_nearby(c):
return 1
else:
return 0
def LF_DG_GENETIC_ABNORMALITIES(c):
"""
This LF searches for key phraes that indicate a genetic abnormality
"""
left_window = " ".join(get_left_tokens(c[0], window=10)) + " ".join(get_left_tokens(c[1], window=10))
right_window = " ".join(get_right_tokens(c[0], window=10)) + " ".join(get_right_tokens(c[1], window=10))
if re.search(ltp(genetic_abnormalities), get_text_between(c), flags=re.I):
return 1
elif re.search(ltp(genetic_abnormalities), left_window, flags=re.I):
return 1
elif re.search(ltp(genetic_abnormalities), right_window, flags=re.I):
return 1
return 0
def LF_DaG_DISEASE_SAMPLE(c):
"""
This LF is designed to look for key phrases that indicate a sentence talking about tissue samples
ex. cell line etc
"""
left_window = " ".join(get_left_tokens(c[0], window=10)) + " ".join(get_left_tokens(c[1], window=10))
right_window = " ".join(get_right_tokens(c[0], window=10)) + " ".join(get_right_tokens(c[1], window=10))
if re.search(ltp(disease_sample_indicators), left_window, flags=re.I):
return 1
elif re.search(ltp(disease_sample_indicators), right_window, flags=re.I):
return 1
else:
return 0
def LF_gene(c):
"""
If candidate has gene word near it
"""
if "gene" in get_left_tokens(c[1]) or "gene" in get_right_tokens(c[1]):
return 1
return 0
def LF_DaG_CELLULAR_ACTIVITY(c):
"""
This LF is designed to look for key phrases that indicate activity within a cell.
e.x. positive immunostating for an experiment
"""
left_window = " ".join(get_left_tokens(c[0], window=10)) + " ".join(get_left_tokens(c[1], window=10))
right_window = " ".join(get_right_tokens(c[0], window=10)) + " ".join(get_right_tokens(c[1], window=10))
if re.search(ltp(cellular_activity), get_tagged_text(c), flags=re.I):
return 1
elif re.search(ltp(cellular_activity), left_window, flags=re.I):
return 1
elif re.search(ltp(cellular_activity), right_window, flags=re.I):
return 1
else:
return 0
def neg_nearby(c):
if (len(negative.intersection(get_between_tokens(c))) > 0):
return True
elif (len(negative.intersection(get_left_tokens(c, window=15))) > 0):
return True
else:
return False
def LF_DEBUG(c):
"""
This label function is for debugging purposes. Feel free to ignore.
keyword arguments:
c - The candidate object to be labeled
"""
print(c)
print()
print("Left Tokens")
print(list(get_left_tokens(c[0], window=5)))
print()
print("Right Tokens")
print(list(get_right_tokens(c[0])))
print()
print("Between Tokens")
print(list(get_between_tokens(c)))
print()
print("Tagged Text")
print(get_tagged_text(c))
print(re.search(r'{{B}} .* is a .* {{A}}', get_tagged_text(c)))
print()
print("Get between Text")
print(get_text_between(c))
print(len(get_text_between(c)))
print()
print("Parent Text")
print(c.get_parent())
print()
return 0
def LF_CD_CHECK_DEPRESSION_USAGE(c):
if "depress" in c[1].get_span():
if re.search(ltp(incorrect_depression_indication), " ".join(get_left_tokens(c[0], window=5)), flags=re.I):
return -1
elif re.search(ltp(incorrect_depression_indication), " ".join(get_right_tokens(c[0], window=5)), flags=re.I):
return -1
return 0
def LF_right_before_or_after(c):
if len(rightbeforeorafter.intersection(set(c[0].parent.words))) == 0:
if len(rightbeforeorafter.intersection(set(get_left_tokens(c[0],window=5, attrib='words')))) > 0 or len(rightbeforeorafter.intersection(set(get_right_tokens(c[0],window=5, attrib='words')))) > 0:
return 1
else:
return 0
else:
return 0
def LF_DaG_NO_ASSOCIATION(c):
"""
This LF is designed to test if there is a key phrase that suggests
a d-g pair is no an association.
"""
left_window = " ".join(get_left_tokens(c[0], window=10)) + " ".join(get_left_tokens(c[1], window=10))
right_window = " ".join(get_right_tokens(c[0], window=10)) + " ".join(get_right_tokens(c[1], window=10))
if LF_DG_METHOD_DESC(c) or LF_DG_TITLE(c):
return 0
elif re.search(ltp(no_direct_association), get_text_between(c), flags=re.I):
return -1
elif re.search(ltp(no_direct_association), left_window, flags=re.I):
return -1
elif re.search(ltp(no_direct_association), right_window, flags=re.I):
return -1
else:
return 0
def LF_variation(c):
"""
If variation keyword in close proximity then label as positive
"""
if len(variation_words.intersection(get_left_tokens(c[1]))) > 0:
return 1
if len(variation_words.intersection(get_right_tokens(c[1]))) > 0:
return 1
return 0
def LF_neg_words(c):
"""
If it mentions serum or intervention before or after gene then negative
"""
if len(neg_words.intersection(get_left_tokens(c[1], window=3))) > 0:
return -1
if len(neg_words.intersection(get_right_tokens(c[1], window=3))) > 0:
return -1
return 0
def LF_disease_context(c):
"""
If mentions cases of or patients with -> disease
"""
tokens = "".join(get_left_tokens(c[1], window=3))
for context in disease_context:
if context in tokens:
return -1
return 0
def LF_CD_PARENTHETICAL_DESC(c):
"""
This label function looks for mentions that are in paranthesis.
Some of the gene mentions are abbreviations rather than names of a gene.
"""
if ")" in c[1].get_span() and "(" in list(get_left_tokens(c[1], window=1)):
if LF_CD_DISTANCE_SHORT(c):
return -1
return 0
def LF_CD_TREATS(c):
if re.search(ltp(treat_indication), get_text_between(c), flags=re.I):
return 1
elif re.search(ltp(treat_indication), " ".join(get_left_tokens(c[0], window=5)), flags=re.I):
return 1
elif re.search(ltp(treat_indication), " ".join(get_right_tokens(c[0], window=5)), flags=re.I):
return 1
else:
return 0
def LF_DaG_WEAK_ASSOCIATION(c):
"""
This label function is design to search for phrases that indicate a
weak association between the disease and gene
"""
left_window = " ".join(get_left_tokens(c[0], window=10)) + " ".join(get_left_tokens(c[1], window=10))
right_window = " ".join(get_right_tokens(c[0], window=10)) + " ".join(get_right_tokens(c[1], window=10))
if LF_DG_METHOD_DESC(c) or LF_DG_TITLE(c):
return 0
elif re.search(ltp(weak_association), get_text_between(c), flags=re.I):
return 1
elif re.search(ltp(weak_association), left_window, flags=re.I):
return 1
elif re.search(ltp(weak_association), right_window, flags=re.I):
return 1
else:
return 0
def LF_DaG_ASSOCIATION(c):
"""
This LF is designed to test if there is a key phrase that suggests
a d-g pair is an association.
"""
left_window = " ".join(get_left_tokens(c[0], window=10)) + " ".join(get_left_tokens(c[1], window=10))
right_window = " ".join(get_right_tokens(c[0], window=10)) + " ".join(get_right_tokens(c[1], window=10))
found_negation = not re.search(r'\b(not|no)\b', left_window, flags=re.I)
if LF_DG_METHOD_DESC(c) or LF_DG_TITLE(c):
return 0
elif re.search(r'(?<!not )(?<!no )' + ltp(direct_association), get_text_between(c), flags=re.I) and found_negation:
return 1
elif re.search(r'(?<!not )(?<!no )' + ltp(direct_association), left_window, flags=re.I) and found_negation:
return 1
elif re.search(r'(?<!not )(?<!no )' + ltp(direct_association), right_window, flags=re.I) and found_negation:
return 1
else:
return 0
def LF_CG_UPREGULATES(c):
"""
This label function is designed to look for phrases
that implies a compound increaseing activity of a gene/protein
"""
if re.search(ltp(upregulates), get_text_between(c), flags=re.I):
return 1
elif upregulates.intersection(get_left_tokens(c[1], window=2)):
return 1
else:
return 0
def LF_CD_WEAKLY_TREATS(c):
"""
This label function is designed to look for phrases
that imply a compound binding to a gene/protein
"""
if re.search(ltp(weak_treatment_indications), get_text_between(c), flags=re.I):
return 1
elif re.search(ltp(weak_treatment_indications), " ".join(get_left_tokens(c[0], window=5)), flags=re.I):
return 1
elif re.search(ltp(weak_treatment_indications), " ".join(get_right_tokens(c[0], window=5)), flags=re.I):
return 1
else:
return 0
def LF_CD_PALLIATES(c):
"""
This label function is designed to look for phrases
that could imply a compound binding to a gene/protein
"""
if re.search(ltp(palliates_indication), get_text_between(c), flags=re.I):
return 1
elif re.search(ltp(palliates_indication), " ".join(get_left_tokens(c[0], window=5)), flags=re.I):
return 1
elif re.search(ltp(palliates_indication), " ".join(get_right_tokens(c[0], window=5)), flags=re.I):
return 1
else:
return 0
def LF_CD_COMPOUND_INDICATION(c):
"""
This label function is designed to look for phrases
that implies a compound increaseing activity of a gene/protein
"""
if re.search(ltp(compound_indications), get_text_between(c), flags=re.I):
return 1
elif re.search(ltp(compound_indications), " ".join(get_left_tokens(c[0], window=5)), flags=re.I):
return 1
elif re.search(ltp(compound_indications), " ".join(get_right_tokens(c[0], window=5)), flags=re.I):
return 1
else:
return 0
def _get_search_func(self, c):
"""
Enumerate the token search space for pattern matching
:param c:
:return:
"""
if self.search == "sentence":
return c.get_parent().__dict__[self.attrib]
elif self.search == "between":
return get_text_between(c).strip().split()
elif self.search == "left":
# use left-most Span
span = c[0] if c[0].char_start < c[1].char_start else c[1]
return get_left_tokens(span, window=self.window, attrib=self.attrib)
elif self.search == "right":
# use right-most Span
span = c[0] if c[0].char_start > c[1].char_start else c[1]
return get_right_tokens(span, window=self.window, attrib=self.attrib)
