def create_message(self):
model = stmts_clj_from_text('MAP2K1 binds MAPK1')
entity = agent_clj_from_text('MAPK1-MAP2K1 complex')
condition_entity = agent_clj_from_text('MAP2K1')
entities = KQMLList([KQMLList([':description', entity])])
pattern = KQMLList()
pattern.set('entities', entities)
pattern.sets('type', 'no_change')
value = KQMLList()
value.sets('type', 'qualitative')
value.sets('value', 'high')
pattern.set('value', value)
content = KQMLList('SATISFIES-PATTERN')
content.set('pattern', pattern)
content.set('model', model)
conditions = KQMLList()
condition = KQMLList()
condition.sets('type', 'multiple')
condition.set('value', '10.0')
quantity = KQMLList()
quantity.sets('type', 'total')
entity = KQMLList()
entity.set('description', condition_entity)
quantity.set('entity', entity)
condition.set('quantity', quantity)
conditions.append(condition)
content.set('conditions', conditions)
msg = get_request(content)
return (msg, content)
def send_display_figure(self, path):
msg = KQMLPerformative('tell')
content = KQMLList('display-image')
content.set('type', 'simulation')
content.sets('path', path)
msg.set('content', content)
self.send(msg)
def create_message_3(self):
content = KQMLList('FIND-CELLULAR-LOCATION')
genes = ekb_from_text('AKT1, MAPK1')
content.sets('genes', str(genes))
msg = get_request(content)
return msg, content
def create_message_failure_2(self):
target = ekb_kstring_from_text('ABC')
content = KQMLList('FIND-CAUSALITY-SOURCE')
content.set('target', target)
content.sets('type', 'phosphorylates')
msg = get_request(content)
return msg, content
def create_message(self):
source = ekb_kstring_from_text('MAPK1')
content = KQMLList('FIND-CAUSALITY-TARGET')
content.set('source', source)
content.sets('type', 'phosphorylation')
msg = get_request(content)
return msg, content
def create_message2(self):
txt = 'ERK activates ELK1. DUSP inactivates ELK1. ' + \
'Active ELK1 transcribes FOS.'
model = stmts_kstring_from_text(txt)
entity = ekb_kstring_from_text('FOS')
entities = KQMLList([KQMLList([':description', entity])])
pattern = KQMLList()
pattern.set('entities', entities)
pattern.sets('type', 'always_value')
value = KQMLList()
value.sets('type', 'qualitative')
value.sets('value', 'low')
pattern.set('value', value)
content = KQMLList('SATISFIES-PATTERN')
content.set('pattern', pattern)
content.set('model', model)
condition_entity = ekb_kstring_from_text('DUSP')
conditions = KQMLList()
condition = KQMLList()
condition.sets('type', 'multiple')
condition.set('value', '100.0')
quantity = KQMLList()
quantity.sets('type', 'total')
entity = KQMLList()
entity.set('description', condition_entity)
quantity.set('entity', entity)
condition.set('quantity', quantity)
conditions.append(condition)
content.set('conditions', conditions)
msg = get_request(content)
return (msg, content)
def create_message_AKT1(self):
content = KQMLList('FIND-GENE-SUMMARY')
genes = ekb_from_text('AKT1')
content.sets('gene', str(genes))
msg = get_request(content)
return msg, content
def create_message(self):
txt = ('MEK not bound to Selumetinib phosphorylates ERK. DUSP '
'dephosphorylates ERK. Selumetinib binds MEK.')
model = stmts_clj_from_text(txt)
entity = agent_clj_from_text('ERK that is phosphorylated')
entities = KQMLList([KQMLList([':description', entity])])
pattern = KQMLList()
pattern.set('entities', entities)
pattern.sets('type', 'no_change')
value = KQMLList()
value.sets('type', 'qualitative')
value.sets('value', 'low')
pattern.set('value', value)
content = KQMLList('SATISFIES-PATTERN')
content.set('pattern', pattern)
content.set('model', model)
condition_entity = agent_clj_from_text('Selumetinib')
conditions = KQMLList()
condition = KQMLList()
condition.sets('type', 'multiple')
condition.set('value', '100.0')
quantity = KQMLList()
quantity.sets('type', 'total')
entity = KQMLList()
entity.set('description', condition_entity)
quantity.set('entity', entity)
condition.set('quantity', quantity)
conditions.append(condition)
content.set('conditions', conditions)
msg = get_request(content)
return msg, content
def make_failure(self, reason=None, description=None):
msg = KQMLList('FAILURE')
if reason:
msg.set('reason', reason)
if description:
msg.sets('description', description)
return msg
def create_message_failure(self):
content = KQMLList('CREATE-TUMOR-BOARD-REPORT')
patient_id = '101'
content.sets('patient-id', patient_id)
msg = get_request(content)
return msg, content
def respond_find_variants_for_genes(self, content):
"""Response content to find-variants-for-genes"""
gene_arg = content.get('GENES')
if not gene_arg:
self.make_failure('MISSING_MECHANISM')
gene_names = _get_kqml_names(gene_arg)
if not gene_names:
return self.make_failure('MISSING_MECHANISM')
disease_arg = content.get('DISEASE')
if not disease_arg:
return self.make_failure('MISSING_MECHANISM')
disease_names = _get_kqml_names(disease_arg)
if not disease_names:
return self.make_failure('INVALID_DISEASE')
disease_name = _sanitize_disase_name(disease_names[0])
disease_abbr = self.BA.get_tcga_abbr(disease_name)
if disease_abbr is None:
return self.make_failure('INVALID_DISEASE')
result = self.BA.find_variants_for_genes_cbio(gene_names, disease_abbr,
"tcga")
if not result:
return self.make_failure('MISSING_MECHANISM')
reply = KQMLList('SUCCESS')
reply.sets('variants', str(result))
return reply
def create_message1(self):
txt = 'ELK1 transcribes FOS.'
model = stmts_clj_from_text(txt)
entity = agent_clj_from_text('FOS')
entities = KQMLList([KQMLList([':description', entity])])
pattern = KQMLList()
pattern.set('entities', entities)
pattern.sets('type', 'eventual_value')
value = KQMLList()
value.sets('type', 'qualitative')
value.sets('value', 'high')
pattern.set('value', value)
content = KQMLList('SATISFIES-PATTERN')
content.set('pattern', pattern)
content.set('model', model)
condition_entity = agent_clj_from_text('MAPK1')
conditions = KQMLList()
condition = KQMLList()
condition.sets('type', 'multiple')
condition.set('value', '100.0')
quantity = KQMLList()
quantity.sets('type', 'total')
entity = KQMLList()
entity.set('description', condition_entity)
quantity.set('entity', entity)
condition.set('quantity', quantity)
conditions.append(condition)
content.set('conditions', conditions)
msg = get_request(content)
return msg, content
def respond_find_mutation_significance(self, content):
"""Response content to find-mutation-significance request"""
gene_arg = content.gets('GENE')
if not gene_arg:
self.make_failure('MISSING_MECHANISM')
gene_names = _get_term_names(gene_arg)
if not gene_names:
return self.make_failure('MISSING_MECHANISM')
gene_name = gene_names[0]
disease_arg = content.gets('DISEASE')
if not disease_arg:
return self.make_failure('MISSING_MECHANISM')
disease_names = _get_term_names(disease_arg)
if not disease_names:
return self.make_failure('INVALID_DISEASE')
disease_name = disease_names[0].replace("-", " ").lower()
disease_abbr = self.CA.get_tcga_abbr(disease_name)
if disease_abbr is None:
return self.make_failure('INVALID_DISEASE')
result = self.CA.find_mutation_significance(gene_name, disease_abbr)
if not result:
return self.make_failure('MISSING_MECHANISM')
reply = KQMLList('SUCCESS')
reply.sets('mutsig', result)
return reply
def send_display_figure(self, path):
msg = KQMLPerformative('tell')
content = KQMLList('display-image')
content.set('type', 'simulation')
content.sets('path', path)
msg.set('content', content)
self.send(msg)
def respond_find_mutation_frequency(self, content):
"""Response content to find-mutation-frequency request"""
gene_arg = content.gets('GENE')
if not gene_arg:
self.make_failure('MISSING_MECHANISM')
gene_names = _get_term_names(gene_arg)
if not gene_names:
return self.make_failure('MISSING_MECHANISM')
gene_name = gene_names[0]
disease_arg = content.gets('DISEASE')
if not disease_arg:
return self.make_failure('MISSING_MECHANISM')
disease_names = _get_term_names(disease_arg)
if not disease_names:
return self.make_failure('INVALID_DISEASE')
disease_name = _sanitize_disase_name(disease_names[0])
disease_abbr = self.CA.get_tcga_abbr(disease_name)
if disease_abbr is None:
return self.make_failure('INVALID_DISEASE')
result = self.CA.find_mutation_frequency(gene_name, disease_abbr)
if not result:
return self.make_failure('MISSING_MECHANISM')
reply = KQMLList('SUCCESS')
reply.sets('mutfreq', result)
return reply
def create_message_failure(self):
target = agent_clj_from_text('BRAF')
content = KQMLList('FIND-CAUSALITY-SOURCE')
content.set('target', target)
content.sets('type', 'activates')
msg = get_request(content)
return msg, content
def create_message(self):
target = agent_clj_from_text('BRAF')
content = KQMLList('FIND-CAUSALITY-SOURCE')
content.set('target', target)
content.sets('type', 'phosphorylation')
msg = get_request(content)
return msg, content
def create_message_failure(self):
source = agent_clj_from_text('MAPK1')
content = KQMLList('FIND-CAUSALITY-TARGET')
content.set('source', source)
content.sets('type', 'activation')
msg = get_request(content)
return msg, content
def make_failure(reason=None, description=None):
msg = KQMLList('FAILURE')
if reason:
msg.set('reason', reason)
if description:
msg.sets('description', description)
return msg
def test_build_from_json():
mm = MRA_Module(testing=True)
st = Phosphorylation(Agent('MEK'), Agent('ERK'))
msg = KQMLList('BUILD-MODEL')
msg.sets('description', json.dumps(stmts_to_json([st])))
msg.sets('format', 'indra_json')
print(msg)
reply = mm.respond_build_model(msg)
def test_choose_nonsense():
bs = BioSense_Module(testing=True)
msg_content = KQMLList('CHOOSE-SENSE')
msg_content.sets('ekb-term', ekb_from_text('bagel'))
res = bs.respond_choose_sense(msg_content)
print(res)
assert res.head() == 'SUCCESS'
assert res.get('agents')[0].gets('ont-type') == None
def test_respond_choose_nonsense():
bs = BioSense_Module(testing=True)
msg_content = KQMLList('CHOOSE-SENSE')
msg_content.sets('ekb-term', ekb_from_text('bagel'))
res = bs.respond_choose_sense(msg_content)
print(res)
assert res.head() == 'SUCCESS'
assert res.get('agents')[0].gets('ont-type') is None
def create_message_1(self):
content = KQMLList('FIND-DRUGS-FOR-MUTATION-DATASET')
genes = ekb_from_text('TP53')
content.sets('genes', str(genes))
content.sets('dataset', "CTRP")
msg = get_request(content)
return msg, content
def _get_drug_kqml(drug_list):
drugs = KQMLList()
for dn, pci in drug_list:
drug = KQMLList()
drug.sets('name', dn.replace(' ', '-'))
if pci:
drug.sets('pubchem_id', pci)
drugs.append(drug)
return drugs
def create_message_failure(self):
source = agent_clj_from_text('MAPK1')
target = agent_clj_from_text('RAS')
content = KQMLList('FIND-CAUSAL-PATH')
content.set('source', source)
content.set('target', target)
content.sets('direction', 'both')
msg = get_request(content)
return msg, content
def _get_drug_kqml(drug_list):
drugs = KQMLList()
for dn, pci in drug_list:
drug = KQMLList()
drug.sets('name', dn.replace(' ', '-'))
if pci:
drug.sets('pubchem_id', pci)
drugs.append(drug)
return drugs
def _get_perf(text, msg_id):
"""Return a request message for a given text."""
msg = KQMLPerformative('REQUEST')
msg.set('receiver', 'READER')
content = KQMLList('run-text')
content.sets('text', text)
msg.set('content', content)
msg.set('reply-with', msg_id)
return msg
def send_null_provenance(self, stmt, for_what, reason=''):
"""Send out that no provenance could be found for a given Statement."""
content_fmt = ('<h4>No supporting evidence found for {statement} from '
'{cause}{reason}.</h4>')
content = KQMLList('add-provenance')
stmt_txt = EnglishAssembler([stmt]).make_model()
content.sets('html', content_fmt.format(statement=stmt_txt,
cause=for_what, reason=reason))
return self.tell(content)
def create_message_failure_3(self):
source = ekb_kstring_from_text('RAS')
target = ekb_kstring_from_text('MAPK1')
content = KQMLList('FIND-CAUSAL-PATH')
content.set('source', source)
content.set('target', target)
content.sets('direction', 'strict')
msg = get_request(content)
return msg, content
def _get_perf(text, msg_id):
text = text.encode('utf-8')
msg = KQMLPerformative('REQUEST')
msg.set('receiver', 'DRUM')
content = KQMLList('run-text')
content.sets('text', text)
msg.set('content', content)
msg.set('reply-with', msg_id)
return msg
def send_null_provenance(self, stmt, for_what, reason=''):
"""Send out that no provenance could be found for a given Statement."""
content_fmt = ('<h4>No supporting evidence found for {statement} from '
'{cause}{reason}.</h4>')
content = KQMLList('add-provenance')
stmt_txt = EnglishAssembler([stmt]).make_model()
content.sets('html', content_fmt.format(statement=stmt_txt,
cause=for_what, reason=reason))
return self.tell(content)
def create_message_1(self):
content = KQMLList('GET-VARIANT-INFO')
gene = agent_clj_from_text('TP53')
content.set('gene', gene)
mutation = 'p.S241F'
content.sets('mutation', mutation)
msg = get_request(content)
return msg, content
def test_choose_sense_what_member():
bs = BioSense_Module(testing=True)
msg_content = KQMLList('CHOOSE-SENSE-WHAT-MEMBER')
msg_content.sets('collection', mek_ekb)
print(msg_content)
res = bs.respond_choose_sense_what_member(msg_content)
print(res)
print(res.head)
assert (res.head() == 'SUCCESS')
assert (len(res.get('members')) == 2)
def test_choose_sense_category():
bs = BioSense_Module(testing=True)
msg_content = KQMLList('CHOOSE-SENSE-CATEGORY')
msg_content.sets('ekb-term', mek1_ekb)
msg_content.sets('category', 'kinase activity')
res = bs.respond_choose_sense_category(msg_content)
print(res)
print(res.head)
assert (res.head() == 'SUCCESS')
assert (res.get('in-category') == 'TRUE')
def test_respond_get_synonyms():
bs = BioSense_Module(testing=True)
msg_content = KQMLList('GET-SYNONYMS')
msg_content.sets('entity', mek1_ekb)
res = bs.respond_get_synonyms(msg_content)
assert res.head() == 'SUCCESS'
syns = res.get('synonyms')
syn_strs = [s.gets(':name') for s in syns]
assert 'MAP2K1' in syn_strs
assert 'MEK1' in syn_strs
assert 'MKK1' in syn_strs
def test_respond_choose_sense_is_member():
bs = BioSense_Module(testing=True)
msg_content = KQMLList('CHOOSE-SENSE-IS-MEMBER')
msg_content.sets('ekb-term', mek1_ekb)
msg_content.sets('collection', mek_ekb)
print(msg_content)
res = bs.respond_choose_sense_is_member(msg_content)
print(res)
print(res.head())
assert(res.head() == 'SUCCESS')
assert(res.get('is-member') == 'TRUE')
def test_choose_sense_is_member():
bs = BioSense_Module(testing=True)
msg_content = KQMLList('CHOOSE-SENSE-IS-MEMBER')
msg_content.sets('ekb-term', mek1_ekb)
msg_content.sets('collection', mek_ekb)
print(msg_content)
res = bs.respond_choose_sense_is_member(msg_content)
print(res)
print(res.head)
assert (res.head() == 'SUCCESS')
assert (res.get('is-member') == 'TRUE')
def report_paths_graph(self, paths_list):
from indra.assemblers.graph import GraphAssembler
from indra.util import flatten
path_stmts = [stmts_from_json(l) for l in paths_list]
all_stmts = flatten(path_stmts)
ga = GraphAssembler(all_stmts)
ga.make_model()
resource = get_img_path('qca_paths.png')
ga.save_pdf(resource)
content = KQMLList('display-image')
content.set('type', 'simulation')
content.sets('path', resource)
self.tell(content)
def send_display_model(self, diagrams):
for diagram_type, resource in diagrams.items():
if not resource:
continue
if diagram_type == 'sbgn':
content = KQMLList('display-sbgn')
content.set('type', diagram_type)
content.sets('graph', resource)
else:
content = KQMLList('display-image')
content.set('type', diagram_type)
content.sets('path', resource)
self.tell(content)
def test_respond_choose_sense():
bs = BioSense_Module(testing=True)
msg_content = KQMLList('CHOOSE-SENSE')
msg_content.sets('ekb-term', mek1_ekb)
res = bs.respond_choose_sense(msg_content)
print(res)
agents = res.get('agents')
assert agents and agents.data
agent = agents[0]
name = agent.gets('name')
assert name == 'MAP2K1'
ont_type = agent.get('ont-type')
assert ont_type == 'ONT::GENE'
def get_ambiguities_msg(ambiguities):
sa = []
for term_id, ambiguity in ambiguities.items():
msg = KQMLList(term_id)
pr = ambiguity[0]['preferred']
pr_dbids = '|'.join([':'.join((k, v)) for
k, v in pr['refs'].items()])
term = KQMLList('term')
term.set('ont-type', pr['type'])
term.sets('ids', pr_dbids)
term.sets('name', pr['name'])
msg.set('preferred', term)
alt = ambiguity[0]['alternative']
alt_dbids = '|'.join([':'.join((k, v)) for
k, v in alt['refs'].items()])
term = KQMLList('term')
term.set('ont-type', alt['type'])
term.sets('ids', alt_dbids)
term.sets('name', alt['name'])
msg.set('alternative', term)
sa.append(msg)
ambiguities_msg = KQMLList(sa)
return ambiguities_msg
def respond_describe_model(self, content):
"""Convert the model to natural language."""
# Get the model.
model_id = self._get_model_id(content)
model = self.mra.get_model_by_id(model_id)
# Turn the model into a text description.
english_assembler = EnglishAssembler(model)
desc = english_assembler.make_model()
# Respond to the BA.
resp = KQMLList('SUCCESS')
resp.sets('description', desc)
return resp
def respond_model_get_json(self, content):
"""Return response content to model-get-json request."""
try:
model_id = self._get_model_id(content)
except Exception:
model_id = None
model = self.mra.get_model_by_id(model_id)
if model is not None:
model_msg = encode_indra_stmts(model)
reply = KQMLList('SUCCESS')
reply.sets('model', model_msg)
else:
reply = self.make_failure('MISSING_MODEL')
return reply
def test_respond_choose_sense_what_member():
bs = BioSense_Module(testing=True)
msg_content = KQMLList('CHOOSE-SENSE-WHAT-MEMBER')
msg_content.sets('collection', mek_ekb)
print(msg_content)
res = bs.respond_choose_sense_what_member(msg_content)
print(res)
print(res.head())
assert(res.head() == 'SUCCESS')
assert(len(res.get('members')) == 2)
m1 = res.get('members')[0]
m2 = res.get('members')[1]
assert m1.gets('name') == 'MAP2K1', m1.gets('name')
assert m2.gets('name') == 'MAP2K2', m2.gets('name')
def get_kagent(agent_tuple, term_id=None):
agent, ont_type, urls = agent_tuple
db_refs = '|'.join('%s:%s' % (k, v) for k, v in
agent.db_refs.items())
kagent = KQMLList(term_id) if term_id else KQMLList()
kagent.sets('name', agent.name)
kagent.sets('ids', db_refs)
url_parts = [KQMLList([':name', KQMLString(k),
':dblink', KQMLString(v)])
for k, v in urls.items()]
url_list = KQMLList()
for url_part in url_parts:
url_list.append(url_part)
kagent.set('id-urls', url_list)
kagent.set('ont-type', ont_type)
return kagent
def respond_model_remove_mechanism(self, content):
"""Return response content to model-remove-mechanism request."""
ekb = content.gets('description')
model_id = self._get_model_id(content)
no_display = content.get('no-display')
try:
res = self.mra.remove_mechanism(ekb, model_id)
except Exception as e:
raise InvalidModelDescriptionError(e)
model_id = res.get('model_id')
if model_id is None:
raise InvalidModelDescriptionError('Could not find model id.')
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(model_id))
# Add the INDRA model json
model = res.get('model')
model_msg = encode_indra_stmts(model)
msg.sets('model', model_msg)
# Handle empty model
if not model:
self.send_clean_model()
# Get the action and add it to the message
removed = res.get('removed')
if not removed:
msg = self.make_failure('REMOVE_FAILED')
return msg
else:
actionl = KQMLList('remove_stmts')
actionl.sets('statements', encode_indra_stmts(removed))
msg.set('action', actionl)
# Add the diagram
diagrams = res.get('diagrams')
logger.info(diagrams)
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
return msg
def respond_get_synonyms(self, content):
"""Respond to a query looking for synonyms of a protein."""
ekb = content.gets('entity')
try:
synonyms = self.bs.get_synonyms(ekb)
except InvalidAgentError:
msg = self.make_failure('INVALID_AGENT')
except SynonymsUnknownError:
msg = self.make_failure('SYNONYMS_UNKNOWN')
else:
syns_kqml = KQMLList()
for s in synonyms:
entry = KQMLList()
entry.sets(':name', s)
syns_kqml.append(entry)
msg = KQMLList('SUCCESS')
msg.set('synonyms', syns_kqml)
return msg
def send_provenance_for_stmts(self, stmt_list, for_what, limit=50,
ev_counts=None):
"""Send out a provenance tell for a list of INDRA Statements.
The message is used to provide evidence supporting a conclusion.
"""
logger.info("Sending provenance for %d statements for \"%s\"."
% (len(stmt_list), for_what))
title = "Supporting evidence from the %s for %s" \
% (self.name, for_what)
content_fmt = '<h4>%s (max %s):</h4>\n%s<hr>'
evidence_html = self._make_report_cols_html(stmt_list, limit=limit,
ev_counts=ev_counts,
title=title)
content = KQMLList('add-provenance')
content.sets('html', content_fmt % (title, limit, evidence_html))
return self.tell(content)
def read_pmc(self, pmcid):
"""Read a given PMC article.
Parameters
----------
pmcid : str
The PMC ID of the article to read. Note that only
articles in the open-access subset of PMC will work.
"""
msg = KQMLPerformative('REQUEST')
msg.set('receiver', 'READER')
content = KQMLList('run-pmcid')
content.sets('pmcid', pmcid)
content.set('reply-when-done', 'true')
msg.set('content', content)
msg.set('reply-with', 'P-%s' % pmcid)
self.reply_counter += 1
self.send(msg)
def respond_find_treatment(self, content):
"""Response content to find-treatment request."""
try:
disease_arg = content.gets('disease')
disease = get_disease(ET.fromstring(disease_arg))
except Exception as e:
logger.error(e)
reply = self.make_failure('INVALID_DISEASE')
return reply
logger.info('Disease type: %s' % disease.disease_type)
if not trips_isa(disease.disease_type, 'ont::cancer'):
logger.info('Disease is not a type of cancer.')
reply = self.make_failure('DISEASE_NOT_FOUND')
return reply
logger.debug('Disease: %s' % disease.name)
try:
mut_protein, mut_percent, agents = \
self.dtda.get_top_mutation(disease.name)
except DiseaseNotFoundException:
reply = self.make_failure('DISEASE_NOT_FOUND')
return reply
# TODO: get functional effect from actual mutations
# TODO: add list of actual mutations to response
# TODO: get fraction not percentage from DTDA
reply = KQMLList('SUCCESS')
protein = KQMLList()
protein.set('name', mut_protein)
protein.set('hgnc', mut_protein)
reply.set('protein', protein)
reply.sets('disease', disease_arg)
reply.set('prevalence', '%.2f' % (mut_percent/100.0))
reply.set('functional-effect', 'ACTIVE')
# These differ only in mutation, which isn't relevant.
an_agent = agents[0]
drug_results = self.dtda.find_target_drugs(an_agent)
drugs = self._get_drug_kqml(drug_results)
reply.set('drugs', drugs)
return reply
def respond_find_drug_targets(self, content):
"""Response content to find-drug-target request."""
try:
drug_arg = content.gets('drug')
drug = self._get_agent(drug_arg)
except Exception as e:
return self.make_failure('INVALID_DRUG')
logger.info('DTDA looking for targets of %s' % drug.name)
drug_targets = self.dtda.find_drug_targets(drug)
all_targets = sorted(list(set(drug_targets)))
reply = KQMLList('SUCCESS')
targets = KQMLList()
for target_name in all_targets:
target = KQMLList()
target.sets('name', target_name)
targets.append(target)
reply.set('targets', targets)
return reply
def test_respond_choose_sense_category():
bs = BioSense_Module(testing=True)
cases = [(mek1_ekb, [('kinase activity', 'TRUE'),
('enzyme', 'TRUE'),
('kinase', 'TRUE'),
('transcription-factor', 'FALSE'),
('W::KINASE', 'TRUE'),
('phosphatase', 'FALSE')]),
(dusp_ekb, [('phosphatase', 'TRUE'), ('enzyme', 'TRUE')]),
(ekb_from_text('BRAF'), [('kinase', 'TRUE')])]
for ekb, result_tuples in cases:
msg_content = KQMLList('CHOOSE-SENSE-CATEGORY')
msg_content.sets('ekb-term', ekb)
for cat, result in result_tuples:
print('Testing: %s. Excpet result %s.' % (cat, result))
msg_content.sets('category', cat)
res = bs.respond_choose_sense_category(msg_content)
print(res)
print(res.head())
assert(res.head() == 'SUCCESS')
assert(res.get('in-category') == result)
def respond_model_undo(self, content):
"""Return response content to model-undo request."""
res = self.mra.model_undo()
no_display = content.get('no-display')
model_id = res.get('model_id')
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', 'NIL' if model_id is None else str(model_id))
# Add the INDRA model json
model = res.get('model')
# Handle empty model
if not model:
self.send_clean_model()
model_msg = encode_indra_stmts(model)
msg.sets('model', model_msg)
# Get the action and add it to the message
action = res.get('action')
actionl = KQMLList()
# Here we handle no action as effectively an empty remove action
if action['action'] in ('no_op', 'remove_stmts'):
actionl.append('remove_stmts')
actionl.sets(
'statements',
encode_indra_stmts(action['statements'])
)
msg.set('action', actionl)
# Add the diagram
diagrams = res.get('diagrams')
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
return msg
def send_model_diagnoses(self, res):
# SUGGESTIONS
# If there is an explanation, english assemble it
expl_path = res.get('explanation_path')
if expl_path:
# Only send this if we haven't already sent an explanation
if not self.have_explanation:
ea_path = EnglishAssembler(expl_path)
path_str = ea_path.make_model()
ea_goal = EnglishAssembler([self.mra.explain])
goal_str = ea_goal.make_model()
if path_str and goal_str:
explanation_str = (
'Our model can now explain how %s: <i>%s</i>' %
(goal_str[:-1], path_str))
content = KQMLList('SPOKEN')
content.sets('WHAT', explanation_str)
self.tell(content)
# If there is a suggestion, say it
suggs = res.get('stmt_suggestions')
if suggs:
say = 'I have some suggestions on how to complete our model.'
say += ' We could try modeling one of:<br>'
stmt_str = '<ul>%s</ul>' % \
''.join([('<li>%s</li>' % EnglishAssembler([stmt]).make_model())
for stmt in suggs])
say += stmt_str
content = KQMLList('SPOKEN')
content.sets('WHAT', say)
self.tell(content)
# If there are corrections
corrs = res.get('stmt_corrections')
if corrs:
stmt = corrs[0]
say = 'It looks like a required activity is missing,'
say += ' consider revising to <i>%s</i>' % \
(EnglishAssembler([stmt]).make_model())
content = KQMLList('SPOKEN')
content.sets('WHAT', say)
self.tell(content)
def create_statements(self):
content = KQMLList('INDRA-TO-NL')
content.sets('statements', json.dumps(stmts_to_json(self.statements)))
return get_request(content), content
def create_message(self):
content = KQMLList('TEST')
content.sets('description', '')
msg = KQMLPerformative('REQUEST')
msg.set('content', content)
return msg, content
