cdr/readme.txt

CDR - Extracting Chemical-Disease Relations from Academic Literature

# Source:
 https://github.com/snorkel-team/snorkel-extraction/tree/master/tutorials/cdr

# Labels:

0: Negative, the drug does NOT induce the disease
1: Positive, the drug induces the disease



33 Label functions (Use ctrl+F to search for implementation)
LFs = [
    LF_c_cause_d,
    LF_c_d,
    LF_c_induced_d,
    LF_c_treat_d,
    LF_c_treat_d_wide,
    LF_closer_chem,
    LF_closer_dis,
    LF_ctd_marker_c_d,
    LF_ctd_marker_induce,
    LF_ctd_therapy_treat,
    LF_ctd_unspecified_treat,
    LF_ctd_unspecified_induce,
    LF_d_following_c,
    LF_d_induced_by_c,
    LF_d_induced_by_c_tight,
    LF_d_treat_c,
    LF_develop_d_following_c,
    LF_far_c_d,
    LF_far_d_c,
    LF_improve_before_disease,
    LF_in_ctd_therapy,
    LF_in_ctd_marker,
    LF_in_patient_with,
    LF_induce,
    LF_induce_name,
    LF_induced_other,
    LF_level,
    LF_measure,
    LF_neg_d,
    LF_risk_d,
    LF_treat_d,
    LF_uncertain,
    LF_weak_assertions,
]


##### Distant supervision approaches
# We'll use the [Comparative Toxicogenomics Database](http://ctdbase.org/) (CTD) for distant supervision.
# The CTD lists chemical-condition entity pairs under three categories: therapy, marker, and unspecified.
# Therapy means the chemical treats the condition, marker means the chemical is typically present with the condition,
# and unspecified is...unspecified. We can write LFs based on these categories.

### LF_in_ctd_unspecified
def LF_in_ctd_unspecified(c):
    return -1 * cand_in_ctd_unspecified(c)

### LF_in_ctd_therapy
def LF_in_ctd_therapy(c):
    return -1 * cand_in_ctd_therapy(c)

### LF_in_ctd_marker 
def LF_in_ctd_marker(c):
    return cand_in_ctd_marker(c)





##### Text pattern approaches
# Now we'll use some LF helpers to create LFs based on indicative text patterns.
# We came up with these rules by using the viewer to examine training candidates and noting frequent patterns.

import re
from snorkel.lf_helpers import (
    get_tagged_text,
    rule_regex_search_tagged_text,
    rule_regex_search_btw_AB,
    rule_regex_search_btw_BA,
    rule_regex_search_before_A,
    rule_regex_search_before_B,
)

# List to parenthetical
def ltp(x):
    return '(' + '|'.join(x) + ')'

### LF_induce
def LF_induce(c):
    return 1 if re.search(r'{{A}}.{0,20}induc.{0,20}{{B}}', get_tagged_text(c), flags=re.I) else 0

### LF_d_induced_by_c
causal_past = ['induced', 'caused', 'due']
def LF_d_induced_by_c(c):
    return rule_regex_search_btw_BA(c, '.{0,50}' + ltp(causal_past) + '.{0,9}(by|to).{0,50}', 1)

### LF_d_induced_by_c_tight
def LF_d_induced_by_c_tight(c):
    return rule_regex_search_btw_BA(c, '.{0,50}' + ltp(causal_past) + ' (by|to) ', 1)

### LF_induce_name
def LF_induce_name(c):
    return 1 if 'induc' in c.chemical.get_span().lower() else 0     

### LF_c_cause_d
causal = ['cause[sd]?', 'induce[sd]?', 'associated with']
def LF_c_cause_d(c):
    return 1 if (
        re.search(r'{{A}}.{0,50} ' + ltp(causal) + '.{0,50}{{B}}', get_tagged_text(c), re.I)
        and not re.search('{{A}}.{0,50}(not|no).{0,20}' + ltp(causal) + '.{0,50}{{B}}', get_tagged_text(c), re.I)
    ) else 0

### LF_d_treat_c
treat = ['treat', 'effective', 'prevent', 'resistant', 'slow', 'promise', 'therap']
def LF_d_treat_c(c):
    return rule_regex_search_btw_BA(c, '.{0,50}' + ltp(treat) + '.{0,50}', -1)

### LF_c_treat_d
def LF_c_treat_d(c):
    return rule_regex_search_btw_AB(c, '.{0,50}' + ltp(treat) + '.{0,50}', -1)

### LF_treat_d
def LF_treat_d(c):
    return rule_regex_search_before_B(c, ltp(treat) + '.{0,50}', -1)

### LF_c_treat_d_wide
def LF_c_treat_d_wide(c):
    return rule_regex_search_btw_AB(c, '.{0,200}' + ltp(treat) + '.{0,200}', -1)

### LF_c_d
def LF_c_d(c):
    return 1 if ('{{A}} {{B}}' in get_tagged_text(c)) else 0

### LF_c_induced_d
def LF_c_induced_d(c):
    return 1 if (
        ('{{A}} {{B}}' in get_tagged_text(c)) and 
        (('-induc' in c[0].get_span().lower()) or ('-assoc' in c[0].get_span().lower()))
        ) else 0

### LF_improve_before_disease
def LF_improve_before_disease(c):
    return rule_regex_search_before_B(c, 'improv.*', -1)

### LF_in_patient_with
pat_terms = ['in a patient with ', 'in patients with']
def LF_in_patient_with(c):
    return -1 if re.search(ltp(pat_terms) + '{{B}}', get_tagged_text(c), flags=re.I) else 0

### LF_uncertain
uncertain = ['combin', 'possible', 'unlikely']
def LF_uncertain(c):
    return rule_regex_search_before_A(c, ltp(uncertain) + '.*', -1)

### LF_induced_other
def LF_induced_other(c):
    return rule_regex_search_tagged_text(c, '{{A}}.{20,1000}-induced {{B}}', -1)

### LF_far_c_d
def LF_far_c_d(c):
    return rule_regex_search_btw_AB(c, '.{100,5000}', -1)

### LF_far_d_c
def LF_far_d_c(c):
    return rule_regex_search_btw_BA(c, '.{100,5000}', -1)

### LF_risk_d
def LF_risk_d(c):
    return rule_regex_search_before_B(c, 'risk of ', 1)

### LF_develop_d_following_c
def LF_develop_d_following_c(c):
    return 1 if re.search(r'develop.{0,25}{{B}}.{0,25}following.{0,25}{{A}}', get_tagged_text(c), flags=re.I) else 0

### LF_d_following_c
procedure, following = ['inject', 'administrat'], ['following']
def LF_d_following_c(c):
    return 1 if re.search('{{B}}.{0,50}' + ltp(following) + '.{0,20}{{A}}.{0,50}' + ltp(procedure), get_tagged_text(c), flags=re.I) else 0

### LF_measure
def LF_measure(c):
    return -1 if re.search('measur.{0,75}{{A}}', get_tagged_text(c), flags=re.I) else 0

### LF_level
def LF_level(c):
    return -1 if re.search('{{A}}.{0,25} level', get_tagged_text(c), flags=re.I) else 0

### LF_neg_d
def LF_neg_d(c):
    return -1 if re.search('(none|not|no) .{0,25}{{B}}', get_tagged_text(c), flags=re.I) else 0

### LF_weak_assertions
WEAK_PHRASES = ['none', 'although', 'was carried out', 'was conducted',
                'seems', 'suggests', 'risk', 'implicated',
               'the aim', 'to (investigate|assess|study)']

WEAK_RGX = r'|'.join(WEAK_PHRASES)
def LF_weak_assertions(c):
    return -1 if re.search(WEAK_RGX, get_tagged_text(c), flags=re.I) else 0






##### Composite LFs

# The following LFs take some of the strongest distant supervision and text pattern LFs,
# and combine them to form more specific LFs. These LFs introduce some obvious 
# dependencies within the LF set, which we will model later.

### LF_ctd_marker_c_d
def LF_ctd_marker_c_d(c):
    return LF_c_d(c) * cand_in_ctd_marker(c)

### LF_ctd_marker_induce
def LF_ctd_marker_induce(c):
    return (LF_c_induced_d(c) or LF_d_induced_by_c_tight(c)) * cand_in_ctd_marker(c)

### LF_ctd_therapy_treat
def LF_ctd_therapy_treat(c):
    return LF_c_treat_d_wide(c) * cand_in_ctd_therapy(c)

### LF_ctd_unspecified_treat
def LF_ctd_unspecified_treat(c):
    return LF_c_treat_d_wide(c) * cand_in_ctd_unspecified(c)

### LF_ctd_unspecified_induce
def LF_ctd_unspecified_induce(c):
    return (LF_c_induced_d(c) or LF_d_induced_by_c_tight(c)) * cand_in_ctd_unspecified(c)






##### Rules based on context hierarchy
# These last two rules will make use of the context hierarchy.
# The first checks if there is a chemical mention much closer to the candidate's disease mention
# than the candidate's chemical mention. The second does the analog for diseases.

### LF_closer_chem
def LF_closer_chem(c):
    # Get distance between chemical and disease
    chem_start, chem_end = c.chemical.get_word_start(), c.chemical.get_word_end()
    dis_start, dis_end = c.disease.get_word_start(), c.disease.get_word_end()
    if dis_start < chem_start:
        dist = chem_start - dis_end
    else:
        dist = dis_start - chem_end
    # Try to find chemical closer than @dist/2 in either direction
    sent = c.get_parent()
    closest_other_chem = float('inf')
    for i in range(dis_end, min(len(sent.words), dis_end + dist // 2)):
        et, cid = sent.entity_types[i], sent.entity_cids[i]
        if et == 'Chemical' and cid != sent.entity_cids[chem_start]:
            return -1
    for i in range(max(0, dis_start - dist // 2), dis_start):
        et, cid = sent.entity_types[i], sent.entity_cids[i]
        if et == 'Chemical' and cid != sent.entity_cids[chem_start]:
            return -1
    return 0

### LF_closer_dis
def LF_closer_dis(c):
    # Get distance between chemical and disease
    chem_start, chem_end = c.chemical.get_word_start(), c.chemical.get_word_end()
    dis_start, dis_end = c.disease.get_word_start(), c.disease.get_word_end()
    if dis_start < chem_start:
        dist = chem_start - dis_end
    else:
        dist = dis_start - chem_end
    # Try to find chemical disease than @dist/8 in either direction
    sent = c.get_parent()
    for i in range(chem_end, min(len(sent.words), chem_end + dist // 8)):
        et, cid = sent.entity_types[i], sent.entity_cids[i]
        if et == 'Disease' and cid != sent.entity_cids[dis_start]:
            return -1
    for i in range(max(0, chem_start - dist // 8), chem_start):
        et, cid = sent.entity_types[i], sent.entity_cids[i]
        if et == 'Disease' and cid != sent.entity_cids[dis_start]:
            return -1
    return 0