def get_path_stmts(results, model, stmts):
all_path_stmts = []
for drug, target, polarity, value, found_path, paths, flag in results:
path_stmts = {}
for path in paths:
path_stmts1 = stmts_from_pysb_path(path, model, stmts)
for ps in path_stmts1:
path_stmts[ps.uuid] = ps
all_path_stmts.append(path_stmts)
return all_path_stmts
def report_paths(scored_paths, model, stmts, cell_line):
"""Report paths for a specific cell line."""
citations = {}
citation_count = 1
ab_name = 'Total c-Jun'
if cell_line == 'C32':
pol = 'decreased'
else:
pol = 'increased'
for drug in scored_paths.keys():
paths = scored_paths[drug]
for path, score in paths[:1]:
title = 'How does %s treatment result in %s %s' % \
(drug, pol, ab_name)
title += ' in %s cells?' % cell_line
print(title)
print('=' * len(title))
path_stmts = stmts_from_pysb_path(path, model, stmts)
sentences = []
for i, stmt in enumerate(path_stmts):
if i == 0:
target = stmt.agent_list()[0].name
sentences.append('%s is a target of %s.' % (target, drug))
# Make citations
pmids = [ev.pmid for ev in stmt.evidence if ev.pmid]
cit_nums = []
for pmid in pmids:
cit_num = citations.get(pmid)
if cit_num is None:
citations[pmid] = citation_count
cit_num = citation_count
citation_count += 1
cit_nums.append(cit_num)
if cit_nums:
cit_nums = sorted(list(set(cit_nums)))
cit_str = ' [%s]' % (','.join([str(c) for c in cit_nums]))
else:
cit_str = ''
ea = EnglishAssembler([stmt])
sentence = ea.make_model()
sentence = sentence[:-1] + cit_str + '.'
sentences.append(sentence)
sentences[-1] = sentences[-1][:-1] + \
', which is measured by %s.' % ab_name
print(' '.join(sentences))
print()
references = 'References\n==========\n'
for k, v in sorted(citations.items(), key=lambda x: x[1]):
references += '[%d] https://www.ncbi.nlm.nih.gov/pubmed/%s\n' % (v, k)
print(references)
def export_paths(scored_paths, model, stmts):
"""Export paths for pathway map in JSON-like format."""
conc = 1.0
time = 10
paths = {}
for cell_line in scored_paths.keys():
for drug in scored_paths[cell_line].keys():
scpaths = scored_paths[cell_line][drug]
path, score = scpaths[0]
label = '%s_%s_%s_%s' % (drug, time, conc, cell_line)
paths[label] = {'meta': [], 'path': []}
path_stmts = stmts_from_pysb_path(path, model, stmts)
uuids = [stmt.uuid for stmt in path_stmts]
paths[label]['path'] = uuids
return paths
def make_english_path(self, mc_type, result):
"""Create an English description of a path."""
paths = []
if result.paths:
for path in result.paths:
sentences = []
if mc_type == 'signed_graph' or mc_type == 'unsigned_graph':
for i in range(len(path[:-1])):
sentence = path[i][0] + ' -> ' + path[i + 1][0]
sentences.append((sentence, ''))
else:
if mc_type == 'pysb':
stmts = stmts_from_pysb_path(
path, self.mc_types['pysb']['model'],
self.model.assembled_stmts)
elif mc_type == 'pybel':
stmts = stmts_from_pybel_path(
path,
self.mc_types['pybel']['model'],
from_db=False,
stmts=self.model.assembled_stmts)
for stmt in stmts:
if not stmt:
continue
if isinstance(stmt, list):
stmt = stmt[0]
ea = EnglishAssembler([stmt])
sentence = ea.make_model()
if self.make_links:
link = get_statement_queries(
[stmt])[0] + '&format=html'
sentences.append((sentence, link))
else:
sentences.append((sentence, ''))
paths.append(sentences)
return paths
def check_explanation(self):
if self.model is None:
raise ValueError('check_explanation requires a PySB model.')
if self.explain is None:
raise ValueError('check_explanation requires an explanation goal.')
result = {}
mc = PysbModelChecker(self.model, [self.explain])
try:
pr = mc.check_statement(self.explain, max_paths=0)
result['has_explanation'] = pr.path_found
except Exception as e:
logger.error("Error checking statement for paths: %s" % str(e))
result['has_explanation'] = False
# If we found a path get a path
if result['has_explanation']:
try:
pr = mc.check_statement(self.explain,
max_paths=1,
max_path_length=8)
path_stmts = stmts_from_pysb_path(pr.paths[0], self.model,
self.statements)
result['explanation_path'] = path_stmts
except Exception as e:
logger.error("Error getting paths for statement: %s" % str(e))
# If we don't already have an explanation, see if we can propose one
else:
# Get the source rules associated with the statement to explain
source_rules = []
subj_mps = grounded_monomer_patterns(self.model,
self.explain.agent_list()[0])
for subj_mp in subj_mps:
source_rules += mc._get_input_rules(subj_mp)
obs_container = mc.stmt_to_obs[self.explain]
# If we've got both source rules and observable names, add dummy
# nodes for the source (connected to all input rules) and the
# target (connected to all observables) so we only have to deal
# with a single source and a single target
if source_rules and obs_container:
new_edges = [('SOURCE', sr) for sr in source_rules]
new_edges += [(on, 'TARGET')
for on, _ in obs_container.main_nodes]
im = mc.get_im()
im.add_edges_from(new_edges)
# Now, we know that there is no path between SOURCE and TARGET.
# Instead, we consider connections among all possible pairs
# of nodes in the graph and count the number of nodes in
# the path between source and target:
best_edge = (None, 0)
for u, v in itertools.permutations(im.nodes(), 2):
# Add the edge to the graph
im.add_edge(u, v)
# Find longest path between source and target
simple_paths = list(
nx.all_simple_paths(im, 'SOURCE', 'TARGET'))
simple_paths.sort(key=lambda p: len(p), reverse=True)
if simple_paths and len(simple_paths[0]) > best_edge[1]:
best_edge = ((u, v), len(simple_paths[0]))
# Now remove the edge we added before going on to the next
im.remove_edge(u, v)
if best_edge[0]:
result['connect_rules'] = best_edge[0]
u_stmt = stmt_from_rule(best_edge[0][0], self.model,
self.statements)
v_stmt = stmt_from_rule(best_edge[0][1], self.model,
self.statements)
if u_stmt and v_stmt:
result['connect_stmts'] = (u_stmt, v_stmt)
logger.info("Model statements: %s" %
str(self.statements))
logger.info("To explain %s, try connecting %s and %s" %
(self.explain, u_stmt, v_stmt))
return result
def path_to_english(path, model, stmts):
path_stmts = stmts_from_pysb_path(path, model, stmts)
ea = EnglishAssembler(path_stmts)
return ea.make_model()