Added more templated quieries

ranking-agent · Sep 26, 2024 · 330be79 · 330be79
1 parent c6dbdc2
commit 330be79
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Showing 5 changed files with 67 additions and 40 deletions.
diff --git a/app.py b/app.py
@@ -18,15 +18,8 @@
 )
 
 server = app.server
-
-
-robokop_link = html.A(
-    id = "robokop-link",
-    children=html.Img(src='/assets/robokop.png',style={'height':'2em','width':'6em','padding-left':'1em'}),
-    href='https://robokop.renci.org/',
-    target='_blank',
-    rel='noopener noreferrer')
-
+app.title = 'EDGAR'
+app._favicon = 'Logo.ico'
 
 colors = {'background': 'white', 'background': '#7794B8', 'dropdown': '#6c6f73', 'text': '#000000'}
 
@@ -108,25 +101,30 @@ def resolvename(name):
     ),
     html.Br(),
     html.Div([
-            html.Div(
-                html.Article([
-                    "This research explores pathway enrichment strategies in biomedical Knowledge Graphs (KGs) as a versatile link-prediction approach, with drug repurposing exemplifying a significant application. Leveraging systems biology, network expression analysis, pathway analysis (PA), and machine learning (ML) methods, KGs aid in uncovering novel interactions among biomedical entities of interest. ",
-                    html.P(),
-                    "While these approaches excel in inferring missing edges within the KG, PA may overlook candidates with similar pathway effects. ",
-                    html.P(),
-                    "By utilizing enrichment-driven analyses on KG data from ROBOKOP, our EDGAR paper applied this method on Alzheimer's disease case study, demonstrating the efficacy of enrichment strategies in linking entities for drug repurposing. Our approach is validated through literature-based evidence derived from clinical trials, showcasing the potential of enrichment-driven strategies in linking biomedical entities."
-                ],
-                    style={'font-size': '20px'}
-                )
-            ),
-            html.Br(),
-            html.Div(
-                robokop_link
+        html.Div(
+            html.Article([
+                "Leveraging systems biology, network expression analysis, pathway analysis (PA), rule learning and machine learning (ML) methods, KGs aid in uncovering novel interactions among biomedical entities of interest. ",
+                "While these approaches excel in inferring missing edges within the KG, they may overlook candidates with similar pathway effects. ",
+                html.P(),
+                "This research explores pathway enrichment strategies in biomedical Knowledge Graphs (KGs) as a versatile link-prediction approach, with drug repurposing exemplifying a significant application in the paper. ",
+                html.P(),
+                "By utilizing enrichment-driven analyses on KG data from ",
+                html.A(
+                    "ROBOKOP",
+                    href='https://robokop.renci.org/',
+                    target='_blank',
+                    rel='noopener noreferrer',
+                    style={'color': 'blue', 'text-decoration': 'underline'}
+                ),
+                ", our EDGAR paper demonstrates the efficacy of enrichment strategies in linking entities for drug repurposing. Our approach is validated through literature-based evidence derived from clinical trials, showcasing the potential of enrichment-driven strategies in linking biomedical entities."
+            ],
+                style={'font-size': '20px'}
             )
-
-        ],
-            style={'background-color': 'whitesmoke', 'display': 'flex', 'flex-direction': 'row',
-                   'align-items': 'center', 'justify-content': 'center'}
+        ),
+        html.Br(),
+    ],
+        style={'background-color': 'whitesmoke', 'display': 'flex', 'flex-direction': 'row',
+               'align-items': 'center', 'justify-content': 'center'}
     ),
 ], className="p-3 bg-body-secondary rounded-3")
 
@@ -242,4 +240,4 @@ def normalizeterm(searchterm, click):
 
 #### Visualize the Output ######
 if __name__ == "__main__":
-    app.run_server(debug=False)
+    app.run_server(debug=True)
diff --git a/assets/styles.css b/assets/styles.css
@@ -3,7 +3,8 @@
 @import url('https://fonts.googleapis.com/css2?family=Cinzel:wght@900&display=swap');
 
 @import url('https://fonts.googleapis.com/css2?family=Oswald:wght@900&display=swap');
-
+
+
 
 body {
   margin: 0;

diff --git a/requirements.txt b/requirements.txt
@@ -19,6 +19,6 @@ PyYAML==6.0.1
 requests==2.32.3
 setuptools==71.0.4
 urllib3==2.2.2
-aiohttp==3.10.1
+aiohttp~=3.10.1
 gunicorn
 
diff --git a/src/edgar_ui.py b/src/edgar_ui.py
@@ -21,7 +21,7 @@
 request_in_progress = False
 response_status = 0
 tk = bmt.Toolkit()
-AC_URL = "https://answercoalesce-test.apps.renci.org/query"
+AC_URL = "https://answercoalesce.renci.org/query"
 all_node_classes = tk.get_all_classes('entity')
 
 
@@ -166,12 +166,16 @@ def send_post_request(data):
                                     dcc.Dropdown(
                                         id="example-query-dropdown",
                                         options=[
-                                            {'label': "What Drugs treats Disease Y?",
+                                            {'label': "What Drugs treats Disease Y eg. MONDO:0004975?",
                                              'value': "biolink:Drug-biolink:treats-biolink:Disease"},
-                                            {'label': "What Genes is associated with Disease X?",
-                                             'value': "biolink:Gene-biolink:associated_with-biolink:Disease"},
-                                            {'label': "What are the Biological Process and Molecular Activities that affects Genes X?",
-                                                'value': "biolink:BioProcessOrActivity-biolink:affects-biolink:Gene"}
+                                            {'label': "What Genes are genetically associated with Disease X eg. DOID:0050430?",
+                                             'value': "biolink:Gene-biolink:genetically_associated_with-biolink:Disease"},
+                                            {'label': "What are the Phenotypes of Disease X eg. MONDO:0005147?",
+                                                'value': "biolink:Disease-biolink:has_phenotype-biolink:PhenotypicFeature"},
+                                            {'label': "What are the Genes that affects Phenotype X eg. HP:0003637?",
+                                                'value': "biolink:Gene-biolink:affects-biolink:PhenotypicFeature"},
+                                            {'label': "What are the Phenotypes of Gene X eg. NCBIGene:122481?",
+                                                'value': "biolink:Gene-biolink:has_phenotype-biolink:PhenotypicFeature"}
                                         ],
                                         value=None,
                                         placeholder='Select an example query pattern...optional',
@@ -409,3 +413,13 @@ def visualize_data(store_data, visualize_nclicks):
             return vizlayout(store_data)
     return ""
 
+
+@callback(
+    Output('output-data', 'children', allow_duplicate=True),
+    [Input('example-query-dropdown', 'value'),
+     Input('source_dropdown', 'value'),
+     Input('predicate_dropdown', 'value'),
+     Input('target_dropdown', 'value')]
+)
+def update_output(selected_query, source_value, predicate_value, target_value):
+    return f'Selected Query: {selected_query}, Source: {source_value}, Predicate: {predicate_value}, Target: {target_value}'
diff --git a/src/visualization.py b/src/visualization.py
@@ -205,6 +205,10 @@ def pickgroup2curieedge(enrichment2group_edge, group2curie_edge, kg_nodes, kg_ed
     terminals = [group2curie_edge['subject'], group2curie_edge['object']]
     finaledges = []
     pvalues = set()
+
+    # print('enrichment2group_edge', enrichment2group_edge)
+    # print()
+    # print('group2curie_edge', group2curie_edge)
     for attributes in enrichment2group_edge["attributes"]:  # ususally one
         enrichment2group_support_graphs = attributes["value"]
         # Each of these exists in the auxiliary graph
@@ -229,10 +233,20 @@ def pickgroup2curieedge(enrichment2group_edge, group2curie_edge, kg_nodes, kg_ed
                         finaledge = [kg_nodes[object_]["name"], group2curie_edge["predicate"],
                                      kg_nodes[next_element]["name"]]
                     elif object_ in terminals:
-                        next_element = terminals[
-                            terminals.index(object_) + 1] if object_ in terminals and terminals.index(
-                            object_) + 1 < len(
-                            object_) else None
+                        try:
+                            next_element = terminals[
+                                terminals.index(object_) + 1] if object_ in terminals and terminals.index(
+                                object_) + 1 < len(
+                                object_) else None
+                        except:
+                            next_element = terminals[
+                                terminals.index(object_)] if object_ in terminals and terminals.index(
+                                object_) + 1 < len(object_) else None
+
+                        # next_element = terminals[
+                        #     terminals.index(object_) + 1] if object_ in terminals and terminals.index(
+                        #     object_) + 1 < len(
+                        #     object_) else None
                         finaledge = [group2curie_edge["predicate"], kg_nodes[next_element]["name"]]
                     finaledges.append(theedge + finaledge)
     return pvalues, finaledges
-Original file line number
+Diff line change
@@ Expand Up / @@ -3,7 +3,8 @@ @@
     @import url('https://fonts.googleapis.com/css2?family=Cinzel:wght@900&display=swap');
     @import url('https://fonts.googleapis.com/css2?family=Oswald:wght@900&display=swap');
     body {
       margin: 0;
@@ Expand Down @@